----------------------------------------

  Demo res cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	94.57536    	129.19075   	-0.07824    	0           
  10          	83.83350    	116.15453   	0.30085     	0           
  100         	39.28776    	56.69675    	0.91552     	0           
  300         	20.49855    	30.60356    	0.97873     	1           
  500         	14.42936    	21.91875    	0.98946     	2           
  1000        	8.40456     	12.98181    	0.99644     	5           
---> try to load best state
              	8.40456     	12.98181    	0.99644     	5                eval state : test_on_restore;
              	8.40456     	12.98181    	0.99644     	5                eval state : test_on_last_epoch;
---> final result
              	8.40456     	12.98181    	0.99644     	5                eval state : final;
----------> finish x-yl training


---------->
res cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	15.35537    	28.90757    	0.98130     	0           
  10          	15.04382    	28.44875    	0.98203     	0           
----------------------------------------

  Demo lar cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	94.57536    	129.19075   	-0.07824    	0           
  10          	83.83350    	116.15453   	0.30085     	0           
  100         	39.28776    	56.69675    	0.91552     	0           
  300         	20.49855    	30.60356    	0.97873     	1           
  500         	14.42936    	21.91875    	0.98946     	1           
  1000        	8.40456     	12.98181    	0.99644     	3           
---> try to load best state
              	8.40456     	12.98181    	0.99644     	3                eval state : test_on_restore;
              	8.40456     	12.98181    	0.99644     	3                eval state : test_on_last_epoch;
---> final result
              	8.40456     	12.98181    	0.99644     	3                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	14.83291    	28.45075    	0.98219     	0           
  10          	14.77323    	28.21255    	0.98245     	0           
----------------------------------------

  Demo res cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00303     	0.00866     	0.94061     	0           
  10          	0.00277     	0.00777     	0.95080     	0           
  100         	0.00135     	0.00417     	0.98123     	0           
  300         	0.00122     	0.00357     	0.98436     	1           
  500         	0.00113     	0.00324     	0.98597     	2           
  1000        	0.00099     	0.00275     	0.98827     	4           
---> try to load best state
              	0.00099     	0.00275     	0.98827     	4                eval state : test_on_restore;
              	0.00099     	0.00275     	0.98827     	4                eval state : test_on_last_epoch;
---> final result
              	0.00099     	0.00275     	0.98827     	4                eval state : final;
----------> finish x-yl training


---------->
res cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	538.23389   	586.32959   	-5626205625.71321	0           
  10          	538.15350   	586.23230   	-5625311810.05157	0           
----------------------------------------

  Demo GAR 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00176     	0.74276     	0.00406     	2.40988            	0           
  10          	0.00247     	0.59157     	0.00492     	2.39262            	0           
  100         	0.00381     	0.58760     	0.00687     	1.40653            	0           
  300         	0.00406     	0.62539     	0.00783     	2.29888            	1           
  500         	0.00438     	0.58866     	0.00871     	3.16455            	2           
  1000        	0.00459     	0.53492     	0.00936     	2.82848            	5           
---> try to load best state
              	0.00459     	0.53492     	0.00936     	2.82848            	5                eval state : test_on_restore;
              	0.00459     	0.53492     	0.00936     	2.82848            	5                eval state : test_on_last_epoch;
---> final result              	0.00459     	0.53492     	0.00936     	2.82848            	5                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00628     	0.62469     	0.01433     	0.33343            	0           
  10          	0.00565     	0.65177     	0.01115     	0.21836            	0           
  100         	0.00663     	0.02598     	0.01205     	-1.76913           	0           
  300         	0.00492     	0.08133     	0.00882     	-2.14831           	2           
  500         	0.00432     	0.65658     	0.00836     	-2.15866           	3           
  1000        	0.00427     	0.63489     	0.00825     	-2.18179           	7           
---> try to load best state
              	0.00427     	0.63489     	0.00825     	-2.18179           	7                eval state : test_on_restore;
              	0.00427     	0.63489     	0.00825     	-2.18179           	7                eval state : test_on_last_epoch;
---> final result
              	0.00427     	0.63489     	0.00825     	-2.18179           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00354     	0.74189     	0.00660     	0.36571            	0           
  10          	0.00360     	0.73801     	0.00658     	0.25855            	0           
  100         	0.00578     	-0.03151    	0.01023     	-1.15320           	1           
  300         	0.00431     	0.58898     	0.00824     	-1.52559           	3           
  500         	0.00440     	0.31954     	0.00811     	-1.17902           	5           
  1000        	0.00412     	0.68356     	0.00806     	-2.05740           	11          
---> try to load best state
              	0.00412     	0.68356     	0.00806     	-2.05740           	11               eval state : test_on_restore;
              	0.00412     	0.68356     	0.00806     	-2.05740           	11               eval state : test_on_last_epoch;
---> final result
              	0.00412     	0.68356     	0.00806     	-2.05740           	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00317     	0.77159     	0.00598     	1.84798            	0           
  10          	0.00337     	0.77556     	0.00635     	0.96972            	0           
  100         	0.00534     	-0.14314    	0.00909     	-0.02177           	1           
  300         	0.01012     	-11.55224   	0.01315     	1.33569            	4           
  500         	0.00377     	0.76398     	0.00750     	0.18511            	7           
  1000        	0.00382     	0.75400     	0.00763     	0.52616            	14          
---> try to load best state
              	0.00382     	0.75400     	0.00763     	0.52616            	14               eval state : test_on_restore;
              	0.00382     	0.75400     	0.00763     	0.52616            	14               eval state : test_on_last_epoch;
---> final result
              	0.00382     	0.75400     	0.00763     	0.52616            	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00310     	0.78236     	0.00596     	2.67330            	0           
  10          	0.00331     	0.78469     	0.00632     	4.51203            	0           
  100         	0.00505     	-1.65198    	0.00831     	4.25651            	2           
  300         	0.00370     	0.77483     	0.00731     	1.43369            	6           
  500         	0.00414     	0.62923     	0.00793     	2.20791            	10          
  epoch 653 reach nan state
---> try to load best state
              	0.00427     	0.61343     	0.00806     	1.42986            	13               eval state : test_on_restore;
              	0.00414     	0.62923     	0.00793     	2.20791            	13               eval state : test_on_last_epoch;
---> final result
              	0.00414     	0.62923     	0.00793     	2.20791            	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end

----------------------------------------

  Demo GAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00239     	0.69451     	0.00564     	4.56966            	0           
  10          	0.00307     	0.60275     	0.00632     	6.24593            	0           
  100         	0.00408     	0.64454     	0.00738     	6.36719            	0           
  300         	0.00430     	0.63118     	0.00814     	4.57493            	1           
  500         	0.00458     	0.58220     	0.00890     	5.01049            	2           
  1000        	0.00473     	0.51805     	0.00941     	4.64089            	4           
---> try to load best state
              	0.00473     	0.51805     	0.00941     	4.64089            	4                eval state : test_on_restore;
              	0.00473     	0.51805     	0.00941     	4.64089            	4                eval state : test_on_last_epoch;
---> final result              	0.00473     	0.51805     	0.00941     	4.64089            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01675     	-0.27726    	0.03761     	0.27629            	0           
  10          	0.01556     	-0.36012    	0.03576     	0.17412            	0           
  100         	0.01103     	-0.22555    	0.02466     	-1.35775           	0           
  300         	0.00562     	0.14692     	0.01011     	-1.58360           	2           
  500         	0.01687     	-27.04679   	0.02285     	-1.51121           	4           
  epoch 688 reach nan state
---> try to load best state
              	0.00502     	0.00019     	0.00916     	-1.54822           	5                eval state : test_on_restore;
              	0.01687     	-27.04679   	0.02285     	-1.51121           	5                eval state : test_on_last_epoch;
---> final result
              	0.00502     	0.00019     	0.00916     	-1.54822           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00674     	-0.00111    	0.01499     	0.87981            	0           
  10          	0.00681     	-0.12874    	0.01741     	0.98399            	0           
  100         	0.00802     	-0.10546    	0.01772     	0.96091            	1           
  300         	0.00532     	0.19937     	0.00951     	0.90037            	3           
  500         	0.00478     	-0.25411    	0.00930     	-0.06729           	5           
  1000        	0.00446     	0.39056     	0.00844     	0.77092            	11          
---> try to load best state
              	0.00446     	0.39056     	0.00844     	0.77092            	11               eval state : test_on_restore;
              	0.00446     	0.39056     	0.00844     	0.77092            	11               eval state : test_on_last_epoch;
---> final result
              	0.00446     	0.39056     	0.00844     	0.77092            	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00336     	0.75038     	0.00620     	3.94267            	0           
  10          	0.00353     	0.75065     	0.00651     	4.02084            	0           
  100         	0.00532     	-0.30047    	0.00919     	5.82946            	1           
  300         	0.00457     	0.51071     	0.00844     	4.01292            	4           
  500         	0.00435     	0.61806     	0.00820     	4.03656            	6           
  1000        	0.00535     	0.55727     	0.00868     	7.07925            	14          
---> try to load best state
              	0.00535     	0.55727     	0.00868     	7.07925            	14               eval state : test_on_restore;
              	0.00535     	0.55727     	0.00868     	7.07925            	14               eval state : test_on_last_epoch;
---> final result
              	0.00535     	0.55727     	0.00868     	7.07925            	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00329     	0.75294     	0.00615     	5.07005            	0           
  10          	0.00347     	0.76016     	0.00646     	6.36297            	0           
  100         	0.00504     	-0.22663    	0.00845     	5.72536            	2           
  300         	0.00388     	0.74683     	0.00757     	4.78593            	6           
  500         	0.00402     	0.71860     	0.00783     	9.30808            	10          
  epoch 628 reach nan state
---> try to load best state
              	0.00392     	0.74419     	0.00767     	7.34771            	12               eval state : test_on_restore;
              	0.00402     	0.71860     	0.00783     	9.30808            	12               eval state : test_on_last_epoch;
---> final result
              	0.00392     	0.74419     	0.00767     	7.34771            	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end

----------------------------------------

  Demo GAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00186     	0.70315     	0.00404     	5.81837            	0           
  10          	0.00253     	0.54559     	0.00478     	7.82776            	0           
  100         	0.00373     	0.55747     	0.00760     	6.22467            	0           
  300         	0.00415     	0.56761     	0.00887     	5.56433            	1           
  500         	0.00449     	0.53049     	0.00965     	6.45052            	2           
  1000        	0.00467     	0.47473     	0.01014     	4.76048            	4           
---> try to load best state
              	0.00467     	0.47473     	0.01014     	4.76048            	4                eval state : test_on_restore;
              	0.00467     	0.47473     	0.01014     	4.76048            	4                eval state : test_on_last_epoch;
---> final result              	0.00467     	0.47473     	0.01014     	4.76048            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01645     	-0.46812    	0.03704     	0.27627            	0           
  10          	0.01533     	-0.68381    	0.03554     	0.17411            	0           
  100         	0.01077     	-0.26782    	0.02478     	-1.35774           	0           
  300         	0.00563     	0.03977     	0.01088     	-1.58363           	2           
  500         	0.01683     	-38.81739   	0.02309     	-1.51129           	4           
  epoch 688 reach nan state
---> try to load best state
              	0.00501     	-0.10488    	0.01010     	-1.54828           	5                eval state : test_on_restore;
              	0.01683     	-38.81739   	0.02309     	-1.51129           	5                eval state : test_on_last_epoch;
---> final result
              	0.00501     	-0.10488    	0.01010     	-1.54828           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00633     	-0.05779    	0.01430     	0.87981            	0           
  10          	0.00656     	-0.18361    	0.01763     	0.98399            	0           
  100         	0.00779     	-0.18599    	0.01787     	0.96090            	1           
  300         	0.00520     	0.07232     	0.00986     	0.90037            	3           
  500         	0.00494     	-0.59359    	0.01087     	-0.06730           	5           
  1000        	0.00453     	0.30816     	0.00986     	0.77092            	10          
---> try to load best state
              	0.00453     	0.30816     	0.00986     	0.77092            	11               eval state : test_on_restore;
              	0.00453     	0.30816     	0.00986     	0.77092            	11               eval state : test_on_last_epoch;
---> final result
              	0.00453     	0.30816     	0.00986     	0.77092            	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00330     	0.69553     	0.00709     	3.94267            	0           
  10          	0.00351     	0.71293     	0.00762     	4.02084            	0           
  100         	0.00533     	-0.87830    	0.01018     	5.82946            	1           
  300         	0.00459     	0.39492     	0.00958     	4.01292            	4           
  500         	0.00440     	0.52573     	0.00940     	4.03656            	7           
  1000        	0.00555     	0.43249     	0.00991     	7.07925            	14          
---> try to load best state
              	0.00555     	0.43249     	0.00991     	7.07925            	14               eval state : test_on_restore;
              	0.00555     	0.43249     	0.00991     	7.07925            	14               eval state : test_on_last_epoch;
---> final result
              	0.00555     	0.43249     	0.00991     	7.07925            	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00324     	0.70204     	0.00705     	5.07005            	0           
  10          	0.00346     	0.72081     	0.00759     	6.36297            	0           
  100         	0.00501     	-0.65203    	0.00940     	5.72536            	2           
  300         	0.00390     	0.68887     	0.00881     	4.78593            	6           
  500         	0.00405     	0.66052     	0.00908     	9.30808            	10          
  epoch 628 reach nan state
---> try to load best state
              	0.00394     	0.70139     	0.00892     	7.34771            	12               eval state : test_on_restore;
              	0.00405     	0.66052     	0.00908     	9.30808            	12               eval state : test_on_last_epoch;
---> final result
              	0.00394     	0.70139     	0.00892     	7.34771            	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end

----------------------------------------

  Demo GAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00178     	0.67521     	0.00410     	4.37469            	0           
  10          	0.00248     	0.40876     	0.00497     	3.06754            	0           
  100         	0.00374     	0.54168     	0.00751     	2.81619            	0           
  300         	0.00410     	0.57608     	0.00848     	4.96180            	1           
  500         	0.00443     	0.53806     	0.00930     	2.41088            	2           
  1000        	0.00464     	0.49283     	0.00988     	2.25072            	5           
---> try to load best state
              	0.00464     	0.49283     	0.00988     	2.25072            	5                eval state : test_on_restore;
              	0.00464     	0.49283     	0.00988     	2.25072            	5                eval state : test_on_last_epoch;
---> final result              	0.00464     	0.49283     	0.00988     	2.25072            	5                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01648     	-0.44496    	0.03710     	0.27627            	0           
  10          	0.01532     	-0.66992    	0.03546     	0.17411            	0           
  100         	0.01077     	-0.33215    	0.02452     	-1.35770           	0           
  300         	0.00553     	-0.05759    	0.01029     	-1.58344           	2           
  500         	0.01682     	-39.52681   	0.02293     	-1.51103           	4           
  epoch 688 reach nan state
---> try to load best state
              	0.00496     	-0.15915    	0.00956     	-1.54803           	5                eval state : test_on_restore;
              	0.01682     	-39.52681   	0.02293     	-1.51103           	5                eval state : test_on_last_epoch;
---> final result
              	0.00496     	-0.15915    	0.00956     	-1.54803           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00642     	-0.10608    	0.01433     	0.87981            	0           
  10          	0.00660     	-0.25100    	0.01734     	0.98399            	0           
  100         	0.00777     	-0.29450    	0.01753     	0.96091            	1           
  300         	0.00514     	0.00460     	0.00942     	0.90037            	3           
  500         	0.00493     	-0.88074    	0.01014     	-0.06728           	5           
  1000        	0.00447     	0.24138     	0.00923     	0.77092            	11          
---> try to load best state
              	0.00447     	0.24138     	0.00923     	0.77092            	11               eval state : test_on_restore;
              	0.00447     	0.24138     	0.00923     	0.77092            	11               eval state : test_on_last_epoch;
---> final result
              	0.00447     	0.24138     	0.00923     	0.77092            	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00326     	0.71345     	0.00656     	3.94267            	0           
  10          	0.00344     	0.71686     	0.00693     	4.02084            	0           
  100         	0.00528     	-1.16552    	0.00959     	5.82946            	1           
  300         	0.00449     	0.37474     	0.00896     	4.01292            	4           
  500         	0.00428     	0.53064     	0.00876     	4.03656            	7           
  1000        	0.00537     	0.44565     	0.00930     	7.07925            	14          
---> try to load best state
              	0.00537     	0.44565     	0.00930     	7.07925            	14               eval state : test_on_restore;
              	0.00537     	0.44565     	0.00930     	7.07925            	14               eval state : test_on_last_epoch;
---> final result
              	0.00537     	0.44565     	0.00930     	7.07925            	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00318     	0.72230     	0.00649     	5.07005            	0           
  10          	0.00338     	0.72642     	0.00688     	6.36297            	0           
  100         	0.00498     	-0.90719    	0.00877     	5.72536            	2           
  300         	0.00380     	0.68320     	0.00811     	4.78593            	6           
  500         	0.00394     	0.65584     	0.00838     	9.30808            	10          
  epoch 628 reach nan state
---> try to load best state
              	0.00384     	0.71301     	0.00823     	7.34771            	13               eval state : test_on_restore;
              	0.00394     	0.65584     	0.00838     	9.30808            	13               eval state : test_on_last_epoch;
---> final result
              	0.00384     	0.71301     	0.00823     	7.34771            	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end

----------------------------------------

  Demo GAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00167     	0.72036     	0.00382     	2.81373            	0           
  10          	0.00234     	0.54513     	0.00464     	3.51534            	0           
  100         	0.00364     	0.52691     	0.00704     	2.35562            	0           
  300         	0.00399     	0.57214     	0.00822     	5.24839            	1           
  500         	0.00434     	0.53694     	0.00910     	2.19382            	2           
  1000        	0.00455     	0.48420     	0.00973     	2.56052            	4           
---> try to load best state
              	0.00455     	0.48420     	0.00973     	2.56052            	4                eval state : test_on_restore;
              	0.00455     	0.48420     	0.00973     	2.56052            	4                eval state : test_on_last_epoch;
---> final result              	0.00455     	0.48420     	0.00973     	2.56052            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01654     	-0.40308    	0.03738     	0.27628            	0           
  10          	0.01539     	-0.59318    	0.03565     	0.17411            	0           
  100         	0.01078     	-0.29318    	0.02453     	-1.35774           	0           
  300         	0.00545     	-0.02371    	0.01018     	-1.58354           	2           
  500         	0.01679     	-38.57207   	0.02289     	-1.51115           	4           
  epoch 688 reach nan state
---> try to load best state
              	0.00485     	-0.11211    	0.00939     	-1.54813           	5                eval state : test_on_restore;
              	0.01679     	-38.57207   	0.02289     	-1.51115           	5                eval state : test_on_last_epoch;
---> final result
              	0.00485     	-0.11211    	0.00939     	-1.54813           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00633     	-0.05265    	0.01447     	0.87981            	0           
  10          	0.00652     	-0.19335    	0.01735     	0.98399            	0           
  100         	0.00772     	-0.24273    	0.01753     	0.96090            	1           
  300         	0.00506     	0.05563     	0.00936     	0.90036            	3           
  500         	0.00475     	-0.71005    	0.00988     	-0.06729           	5           
  1000        	0.00432     	0.29418     	0.00895     	0.77092            	10          
---> try to load best state
              	0.00432     	0.29418     	0.00895     	0.77092            	10               eval state : test_on_restore;
              	0.00432     	0.29418     	0.00895     	0.77092            	10               eval state : test_on_last_epoch;
---> final result
              	0.00432     	0.29418     	0.00895     	0.77092            	10               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00314     	0.72766     	0.00637     	3.94267            	0           
  10          	0.00331     	0.72436     	0.00673     	4.02084            	0           
  100         	0.00518     	-0.95133    	0.00940     	5.82946            	1           
  300         	0.00438     	0.43152     	0.00874     	4.01292            	4           
  500         	0.00416     	0.55824     	0.00852     	4.03656            	7           
  1000        	0.00517     	0.48564     	0.00902     	7.07925            	14          
---> try to load best state
              	0.00517     	0.48564     	0.00902     	7.07925            	14               eval state : test_on_restore;
              	0.00517     	0.48564     	0.00902     	7.07925            	14               eval state : test_on_last_epoch;
---> final result
              	0.00517     	0.48564     	0.00902     	7.07925            	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00307     	0.73065     	0.00631     	5.07005            	0           
  10          	0.00325     	0.73368     	0.00667     	6.36297            	0           
  100         	0.00488     	-0.72820    	0.00857     	5.72536            	2           
  300         	0.00367     	0.70328     	0.00785     	4.78593            	6           
  500         	0.00382     	0.67799     	0.00812     	9.30808            	10          
  epoch 628 reach nan state
---> try to load best state
              	0.00371     	0.72683     	0.00796     	7.34771            	13               eval state : test_on_restore;
              	0.00382     	0.67799     	0.00812     	9.30808            	13               eval state : test_on_last_epoch;
---> final result
              	0.00371     	0.72683     	0.00796     	7.34771            	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end

----------------------------------------

  Demo GAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00168     	0.65488     	0.00409     	5.97039            	0           
  10          	0.00232     	0.52330     	0.00512     	7.99518            	0           
  100         	0.00391     	0.48369     	0.00855     	9.44525            	0           
  300         	0.00437     	0.50145     	0.00983     	5.49579            	1           
  500         	0.00476     	0.44513     	0.01071     	5.87881            	2           
  1000        	0.00502     	0.32184     	0.01140     	6.34595            	4           
---> try to load best state
              	0.00502     	0.32184     	0.01140     	6.34595            	4                eval state : test_on_restore;
              	0.00502     	0.32184     	0.01140     	6.34595            	4                eval state : test_on_last_epoch;
---> final result              	0.00502     	0.32184     	0.01140     	6.34595            	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.01670     	-0.54987    	0.03754     	0.27628            	0           
  10          	0.01562     	-0.70520    	0.03594     	0.17412            	0           
  100         	0.01093     	-0.28452    	0.02508     	-1.35771           	0           
  300         	0.00589     	-0.07024    	0.01188     	-1.58353           	2           
  500         	0.01721     	-44.73525   	0.02365     	-1.51120           	4           
  epoch 688 reach nan state
---> try to load best state
              	0.00529     	-0.17891    	0.01127     	-1.54816           	5                eval state : test_on_restore;
              	0.01721     	-44.73525   	0.02365     	-1.51120           	5                eval state : test_on_last_epoch;
---> final result
              	0.00529     	-0.17891    	0.01127     	-1.54816           	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00638     	-0.03275    	0.01470     	0.87981            	0           
  10          	0.00665     	-0.15663    	0.01799     	0.98399            	0           
  100         	0.00791     	-0.19944    	0.01830     	0.96090            	1           
  300         	0.00547     	0.05092     	0.01102     	0.90036            	3           
  500         	0.00518     	-0.72158    	0.01191     	-0.06729           	5           
  1000        	0.00480     	0.26970     	0.01104     	0.77092            	10          
---> try to load best state
              	0.00480     	0.26970     	0.01104     	0.77092            	11               eval state : test_on_restore;
              	0.00480     	0.26970     	0.01104     	0.77092            	11               eval state : test_on_last_epoch;
---> final result
              	0.00480     	0.26970     	0.01104     	0.77092            	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00353     	0.64937     	0.00814     	3.94267            	0           
  10          	0.00375     	0.66489     	0.00875     	4.02084            	0           
  100         	0.00556     	-0.96831    	0.01116     	5.82946            	1           
  300         	0.00488     	0.38773     	0.01079     	4.01292            	4           
  500         	0.00468     	0.49438     	0.01063     	4.03656            	6           
  1000        	0.00572     	0.38745     	0.01105     	7.07925            	14          
---> try to load best state
              	0.00572     	0.38745     	0.01105     	7.07925            	14               eval state : test_on_restore;
              	0.00572     	0.38745     	0.01105     	7.07925            	14               eval state : test_on_last_epoch;
---> final result
              	0.00572     	0.38745     	0.01105     	7.07925            	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.00347     	0.65880     	0.00811     	5.07005            	0           
  10          	0.00372     	0.67189     	0.00871     	6.36297            	0           
  100         	0.00524     	-0.83109    	0.01041     	5.72536            	2           
  300         	0.00418     	0.62809     	0.01008     	4.78593            	6           
  500         	0.00433     	0.61568     	0.01031     	9.30808            	10          
  epoch 628 reach nan state
---> try to load best state
              	0.00422     	0.65141     	0.01018     	7.34771            	12               eval state : test_on_restore;
              	0.00433     	0.61568     	0.01031     	9.30808            	12               eval state : test_on_last_epoch;
---> final result
              	0.00422     	0.65141     	0.01018     	7.34771            	13               eval state : final;module_name : GAR;cp_record_file : True;
