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

  Demo GAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
----------------------------------------

  Demo GAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	121.37878   	-0.01589    	215.91412   	0           
  10          	108.41674   	0.08951     	193.06242   	1           
  100         	62.38475    	0.31634     	116.61071   	8           
----------------------------------------

  Demo GAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	121.37878   	-0.01589    	215.91412   	0           
  10          	108.41674   	0.08951     	193.06242   	1           
  100         	62.38475    	0.31634     	116.61071   	8           
  300         	41.17559    	0.28314     	82.84930    	23          
  500         	19.94474    	0.40790     	40.34336    	38          
---> try to load best state
              	34.63740    	-275.60342  	49.57596    	61               eval state : test_on_restore;
              	292.19788   	-331.70919  	373.35272   	61               eval state : test_on_last_epoch;
---> final result              	34.63740    	-275.60342  	49.57596    	61               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	507.47305   	-31.97860   	731.39612   	0           
  10          	169.07010   	-28.48246   	241.46130   	0           
  100         	38.15906    	-52.43242   	53.57641    	1           
  300         	37.34981    	-196.75969  	53.88548    	4           
  500         	37.46188    	-81.82478   	54.07430    	7           
  epoch 956 reach nan state
---> try to load best state
              	175.73651   	-2.11486    	320.09695   	13               eval state : test_on_restore;
              	37.46188    	-81.82478   	54.07430    	14               eval state : test_on_last_epoch;
---> final result
              	37.46188    	-81.82478   	54.07430    	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	392.79190   	-8.77163    	580.90784   	0           
  10          	163.29895   	-8.78128    	248.07312   	0           
  100         	45.41899    	-52.49679   	69.22575    	2           
  300         	37.75292    	-72.51305   	55.42963    	6           
  500         	35.97572    	-65.99309   	51.82607    	10          
  epoch 862 reach nan state
---> try to load best state
              	93.89819    	-0.23274    	177.54681   	17               eval state : test_on_restore;
              	35.97572    	-65.99309   	51.82607    	17               eval state : test_on_last_epoch;
---> final result
              	35.97572    	-65.99309   	51.82607    	17               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	273.17389   	-3.60398    	417.41318   	0           
  10          	111.10348   	-6.16863    	163.70793   	0           
  100         	39.65192    	-49.76204   	55.91741    	3           
  300         	37.23200    	-51.51405   	53.45076    	8           
  500         	33.58422    	-9.94752    	49.11073    	14          
  epoch 753 reach nan state
---> try to load best state
              	30.88256    	0.22289     	59.76825    	21               eval state : test_on_restore;
              	33.58422    	-9.94752    	49.11073    	21               eval state : test_on_last_epoch;
---> final result
              	33.58422    	-9.94752    	49.11073    	22               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	169.53728   	-2.00121    	262.84772   	0           
  10          	81.36812    	-5.66767    	121.63307   	0           
  100         	36.62864    	-44.78784   	52.44090    	5           
  300         	35.89429    	-31.28532   	51.56768    	15          
  500         	27.21986    	-1.79277    	45.39531    	26          
  epoch 728 reach nan state
---> try to load best state
              	20.89612    	0.28415     	40.79224    	37               eval state : test_on_restore;
              	27.21986    	-1.79277    	45.39531    	37               eval state : test_on_last_epoch;
---> final result
              	20.89612    	0.28415     	40.79224    	37               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	123.54995   	-1.12582    	202.73604   	0           
  10          	74.75739    	-3.57602    	114.58878   	1           
  100         	37.38295    	-46.73223   	54.05046    	10          
  300         	34.31408    	-29.18839   	49.44598    	29          
  500         	25.42128    	-2.96372    	42.55316    	48          
  epoch 855 reach nan state
---> try to load best state
              	23.63894    	0.39908     	43.15401    	82               eval state : test_on_restore;
              	25.42128    	-2.96372    	42.55316    	82               eval state : test_on_last_epoch;
---> final result
              	25.42128    	-2.96372    	42.55316    	82               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	124.30193   	-0.01048    	219.35298   	0           
  10          	111.24573   	0.10383     	195.22925   	0           
  100         	64.24220    	0.31617     	118.41850   	7           
  300         	41.81589    	0.29031     	83.34843    	22          
  500         	20.49156    	0.41645     	41.12227    	36          
---> try to load best state
              	35.39349    	-267.32293  	49.71182    	56               eval state : test_on_restore;
              	294.00296   	-331.33365  	375.86270   	56               eval state : test_on_last_epoch;
---> final result              	35.39349    	-267.32293  	49.71182    	56               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	499.99680   	-16.00312   	667.81067   	0           
  10          	166.59109   	-11.17698   	232.61562   	0           
  100         	37.06130    	-53.98719   	50.93140    	1           
  300         	40.07061    	-131.63306  	56.28732    	3           
  500         	35.76332    	-49.20608   	49.19197    	5           
  epoch 941 reach nan state
---> try to load best state
              	201.18428   	-1.24437    	291.63477   	10               eval state : test_on_restore;
              	35.76332    	-49.20608   	49.19197    	10               eval state : test_on_last_epoch;
---> final result
              	35.76332    	-49.20608   	49.19197    	10               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	392.82520   	-7.58705    	573.46686   	0           
  10          	147.29056   	-6.44140    	222.20328   	0           
  100         	43.92400    	-66.34174   	65.40777    	1           
  300         	38.74556    	-104.61691  	55.09882    	4           
  500         	36.89958    	-36.10401   	51.83835    	7           
  epoch 796 reach nan state
---> try to load best state
              	101.33578   	-0.07749    	169.99261   	12               eval state : test_on_restore;
              	36.89958    	-36.10401   	51.83835    	12               eval state : test_on_last_epoch;
---> final result
              	36.89958    	-36.10401   	51.83835    	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	234.69095   	-5.04700    	406.72174   	0           
  10          	94.95214    	-7.34196    	151.75168   	0           
  100         	38.76716    	-80.05416   	55.03202    	2           
  300         	38.44482    	-76.99405   	54.52806    	6           
  500         	35.29961    	-69.37115   	50.79178    	11          
  epoch 846 reach nan state
---> try to load best state
              	78.77641    	0.30966     	153.78606   	18               eval state : test_on_restore;
              	35.29961    	-69.37115   	50.79178    	18               eval state : test_on_last_epoch;
---> final result
              	35.29961    	-69.37115   	50.79178    	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	222.52870   	-3.39008    	376.90689   	0           
  10          	96.24996    	-3.00206    	156.57304   	0           
  100         	38.65657    	-48.51095   	54.94145    	4           
  300         	35.87159    	-38.17397   	50.76672    	12          
  500         	30.05058    	-14.53421   	45.66823    	22          
  epoch 650 reach nan state
---> try to load best state
              	21.25010    	-0.20594    	41.07545    	29               eval state : test_on_restore;
              	30.05058    	-14.53421   	45.66823    	29               eval state : test_on_last_epoch;
---> final result
              	21.25010    	-0.20594    	41.07545    	29               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	124.38001   	-1.26585    	203.67215   	0           
  10          	75.33981    	-3.75633    	119.47791   	1           
  100         	37.89998    	-44.91327   	54.08208    	9           
  300         	35.65105    	-36.38219   	50.54515    	28          
  500         	27.95391    	-11.63018   	44.09959    	47          
  epoch 677 reach nan state
---> try to load best state
              	11.79584    	0.43803     	22.55781    	64               eval state : test_on_restore;
              	27.95391    	-11.63018   	44.09959    	64               eval state : test_on_last_epoch;
---> final result
              	11.79584    	0.43803     	22.55781    	64               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	136.06305   	-0.12621    	253.40358   	0           
  10          	121.44641   	-0.00187    	226.41896   	0           
  100         	67.17473    	0.28435     	125.24270   	7           
  300         	42.95372    	0.29910     	85.96014    	23          
  500         	21.19757    	0.42934     	45.75372    	38          
---> try to load best state
              	38.73581    	-440.20758  	54.92535    	61               eval state : test_on_restore;
              	325.21774   	-331.01107  	415.62988   	61               eval state : test_on_last_epoch;
---> final result              	38.73581    	-440.20758  	54.92535    	61               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	526.19757   	-16.73583   	761.22162   	0           
  10          	168.22141   	-12.68796   	244.31340   	0           
  100         	40.69977    	-165.31772  	61.13006    	1           
  300         	38.93399    	-212.37903  	56.73359    	4           
  500         	41.10668    	-112.36723  	60.65226    	7           
  1000        	207.88637   	-1.47149    	396.11349   	14          
---> try to load best state
              	207.88637   	-1.47149    	396.11349   	14               eval state : test_on_restore;
              	207.88637   	-1.47149    	396.11349   	14               eval state : test_on_last_epoch;
---> final result
              	207.88637   	-1.47149    	396.11349   	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	493.32095   	-12.81923   	749.62427   	0           
  10          	144.15802   	-6.87325    	216.12271   	0           
  100         	40.91841    	-145.62138  	60.21055    	2           
  300         	37.91740    	-44.50873   	55.65256    	6           
  500         	38.34635    	-84.06521   	56.35427    	10          
  epoch 884 reach nan state
---> try to load best state
              	57.70101    	0.17980     	137.71202   	17               eval state : test_on_restore;
              	38.34635    	-84.06521   	56.35427    	17               eval state : test_on_last_epoch;
---> final result
              	38.34635    	-84.06521   	56.35427    	17               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	293.58954   	-4.53961    	457.78363   	0           
  10          	108.99231   	-7.95334    	163.79941   	0           
  100         	41.85361    	-71.15484   	60.15461    	2           
  300         	39.02104    	-57.34189   	55.47232    	8           
  500         	37.71955    	-77.37024   	54.29712    	14          
  epoch 774 reach nan state
---> try to load best state
              	30.15195    	0.30004     	61.22447    	21               eval state : test_on_restore;
              	37.71955    	-77.37024   	54.29712    	21               eval state : test_on_last_epoch;
---> final result
              	37.71955    	-77.37024   	54.29712    	22               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	199.70081   	-2.37849    	326.97409   	0           
  10          	90.98798    	-5.66845    	137.83913   	0           
  100         	41.87867    	-49.61920   	60.34394    	5           
  300         	39.64629    	-35.37097   	57.24054    	15          
  500         	36.20103    	-27.71084   	54.96337    	25          
  epoch 737 reach nan state
---> try to load best state
              	17.77796    	0.13144     	38.32582    	36               eval state : test_on_restore;
              	36.20103    	-27.71084   	54.96337    	36               eval state : test_on_last_epoch;
---> final result
              	17.77796    	0.13144     	38.32582    	36               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	137.33803   	-1.16798    	232.69164   	0           
  10          	84.95440    	-4.94172    	130.17172   	1           
  100         	41.32204    	-49.78254   	60.21498    	9           
  300         	38.57314    	-31.43812   	56.25949    	29          
  500         	31.93268    	-11.93379   	54.78833    	48          
  epoch 917 reach nan state
---> try to load best state
              	46.12014    	0.33072     	89.05150    	87               eval state : test_on_restore;
              	31.93268    	-11.93379   	54.78833    	87               eval state : test_on_last_epoch;
---> final result
              	31.93268    	-11.93379   	54.78833    	87               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	145.16185   	-0.16328    	271.84879   	0           
  10          	128.87535   	-0.03317    	243.82260   	0           
  100         	70.36440    	0.26964     	134.22212   	7           
  300         	43.98295    	0.30713     	87.79520    	23          
  500         	22.09428    	0.42310     	46.55592    	38          
---> try to load best state
              	37.39980    	-153.52877  	54.71452    	61               eval state : test_on_restore;
              	296.55164   	-321.98534  	374.16135   	61               eval state : test_on_last_epoch;
---> final result              	37.39980    	-153.52877  	54.71452    	61               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	653.03943   	-119.64892  	847.29767   	0           
  10          	182.23923   	-46.91892   	251.56671   	0           
  100         	40.67360    	-61.03177   	63.93014    	1           
  300         	43.29718    	-59.36333   	64.24380    	4           
  500         	45.42078    	-10.34200   	69.86443    	7           
  epoch 877 reach nan state
---> try to load best state
              	243.58032   	-1.42774    	395.34518   	13               eval state : test_on_restore;
              	45.42078    	-10.34200   	69.86443    	13               eval state : test_on_last_epoch;
---> final result
              	45.42078    	-10.34200   	69.86443    	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	487.13171   	-10.00539   	760.72583   	0           
  10          	137.24991   	-4.54593    	206.26756   	0           
  100         	40.50214    	-81.20522   	61.16336    	2           
  300         	36.56556    	-24.60073   	55.61591    	6           
  500         	37.12162    	-37.54768   	56.49103    	10          
  epoch 891 reach nan state
---> try to load best state
              	206.80191   	-0.67180    	397.57184   	18               eval state : test_on_restore;
              	37.12162    	-37.54768   	56.49103    	18               eval state : test_on_last_epoch;
---> final result
              	37.12162    	-37.54768   	56.49103    	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	380.26785   	-3.33832    	591.98242   	0           
  10          	119.57955   	-2.47480    	177.94072   	0           
  100         	40.64480    	-62.76696   	63.06531    	2           
  300         	39.68562    	-42.41875   	60.49587    	8           
  500         	38.37280    	-43.88987   	59.24153    	14          
  epoch 767 reach nan state
---> try to load best state
              	34.68351    	-0.86090    	82.62936    	22               eval state : test_on_restore;
              	38.37280    	-43.88987   	59.24153    	22               eval state : test_on_last_epoch;
---> final result
              	38.37280    	-43.88987   	59.24153    	22               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	251.34184   	-1.72189    	405.19034   	0           
  10          	104.20048   	-3.34622    	155.62253   	0           
  100         	41.46561    	-54.29167   	62.28285    	5           
  300         	38.92683    	-32.29790   	57.53814    	15          
  500         	33.45835    	-12.41859   	50.95390    	24          
  epoch 732 reach nan state
---> try to load best state
              	21.16258    	0.07642     	44.15816    	35               eval state : test_on_restore;
              	33.45835    	-12.41859   	50.95390    	35               eval state : test_on_last_epoch;
---> final result
              	21.16258    	0.07642     	44.15816    	35               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	148.00803   	-0.97307    	257.59061   	0           
  10          	86.96114    	-4.89118    	132.98761   	1           
  100         	39.23988    	-40.22985   	58.55641    	9           
  300         	36.75025    	-25.61481   	53.84003    	29          
  500         	31.54717    	-17.16464   	49.25823    	48          
  epoch 722 reach nan state
---> try to load best state
              	18.35503    	0.20047     	39.41364    	69               eval state : test_on_restore;
              	31.54717    	-17.16464   	49.25823    	69               eval state : test_on_last_epoch;
---> final result
              	18.35503    	0.20047     	39.41364    	69               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	127.47914   	-0.02388    	230.52594   	0           
  10          	115.07131   	0.08219     	209.03705   	0           
  100         	64.96862    	0.30976     	122.10078   	7           
  300         	41.98303    	0.29810     	84.28191    	23          
  500         	20.73507    	0.41652     	42.01804    	38          
---> try to load best state
              	37.36748    	-283.60303  	52.89936    	62               eval state : test_on_restore;
              	309.47910   	-309.60647  	399.92395   	62               eval state : test_on_last_epoch;
---> final result              	37.36748    	-283.60303  	52.89936    	62               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	464.88754   	-37.60864   	720.87549   	0           
  10          	148.82872   	-15.06458   	227.56155   	0           
  100         	38.97386    	-79.41965   	56.12371    	1           
  300         	39.52630    	-64.88723   	57.46568    	4           
  500         	36.01322    	-24.54658   	52.23647    	7           
  epoch 986 reach nan state
---> try to load best state
              	449.58975   	-9.44671    	717.93457   	14               eval state : test_on_restore;
              	36.01322    	-24.54658   	52.23647    	14               eval state : test_on_last_epoch;
---> final result
              	36.01322    	-24.54658   	52.23647    	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	400.37500   	-15.85847   	630.91101   	0           
  10          	133.76781   	-10.81226   	206.97304   	0           
  100         	48.58805    	-31.82024   	71.51324    	2           
  300         	40.20500    	-87.38013   	57.56215    	6           
  500         	35.42520    	-31.45817   	50.97678    	10          
  epoch 750 reach nan state
---> try to load best state
              	90.24487    	-0.03656    	177.83907   	14               eval state : test_on_restore;
              	35.42520    	-31.45817   	50.97678    	14               eval state : test_on_last_epoch;
---> final result
              	35.42520    	-31.45817   	50.97678    	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	295.94696   	-3.92614    	481.89404   	0           
  10          	111.07934   	-3.93759    	181.87585   	0           
  100         	44.31092    	-23.92925   	64.64567    	3           
  300         	38.53764    	-49.58665   	54.87847    	8           
  500         	35.80283    	-45.59861   	50.73169    	14          
  epoch 811 reach nan state
---> try to load best state
              	74.52978    	0.16823     	159.73438   	23               eval state : test_on_restore;
              	35.80283    	-45.59861   	50.73169    	23               eval state : test_on_last_epoch;
---> final result
              	35.80283    	-45.59861   	50.73169    	23               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	211.30130   	-2.79165    	366.73944   	0           
  10          	92.67173    	-2.25765    	153.11790   	0           
  100         	44.13095    	-44.04227   	62.52628    	5           
  300         	38.45325    	-41.95563   	54.90369    	15          
  500         	31.39453    	-5.46920    	48.83480    	24          
  epoch 788 reach nan state
---> try to load best state
              	24.00683    	0.17766     	52.08894    	38               eval state : test_on_restore;
              	31.39453    	-5.46920    	48.83480    	38               eval state : test_on_last_epoch;
---> final result
              	31.39453    	-5.46920    	48.83480    	38               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	125.67735   	-1.61595    	210.64795   	0           
  10          	76.22820    	-3.10052    	122.03005   	1           
  100         	39.14545    	-48.22188   	55.93772    	10          
  300         	37.92107    	-34.95781   	53.63348    	29          
  500         	27.63457    	-3.19583    	47.63776    	48          
  epoch 742 reach nan state
---> try to load best state
              	15.77226    	0.40016     	30.01483    	71               eval state : test_on_restore;
              	27.63457    	-3.19583    	47.63776    	71               eval state : test_on_last_epoch;
---> final result
              	15.77226    	0.40016     	30.01483    	71               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	119.89771   	-0.05101    	209.53558   	0           
  10          	107.28570   	0.06956     	188.87323   	0           
  100         	63.69216    	0.31737     	116.66394   	7           
  300         	41.93490    	0.29772     	82.63111    	23          
  500         	20.73318    	0.41584     	40.98289    	38          
---> try to load best state
              	35.80900    	-271.30318  	50.54273    	61               eval state : test_on_restore;
              	294.37726   	-311.87169  	373.36200   	61               eval state : test_on_last_epoch;
---> final result              	35.80900    	-271.30318  	50.54273    	61               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	605.18262   	-163.65666  	858.62006   	0           
  10          	175.43146   	-42.46003   	255.01691   	0           
  100         	45.33995    	-190.93706  	77.73911    	1           
  300         	51.97013    	-48.09870   	83.20409    	3           
  500         	44.31816    	-35.61669   	74.28185    	6           
  epoch 901 reach nan state
---> try to load best state
              	501.67319   	-12.21347   	805.16010   	10               eval state : test_on_restore;
              	44.31816    	-35.61669   	74.28185    	10               eval state : test_on_last_epoch;
---> final result
              	44.31816    	-35.61669   	74.28185    	10               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	340.42270   	-12.61865   	522.75659   	0           
  10          	140.02646   	-7.35292    	214.85432   	0           
  100         	44.79289    	-33.09949   	66.90084    	1           
  300         	36.82644    	-66.90165   	54.13784    	5           
  500         	34.64374    	-38.27645   	50.57601    	8           
  epoch 929 reach nan state
---> try to load best state
              	112.51994   	-0.12837    	211.05495   	16               eval state : test_on_restore;
              	34.64374    	-38.27645   	50.57601    	16               eval state : test_on_last_epoch;
---> final result
              	34.64374    	-38.27645   	50.57601    	16               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	254.15491   	-4.88164    	421.36023   	0           
  10          	104.29063   	-7.85468    	161.93137   	0           
  100         	40.73064    	-62.54784   	58.07253    	2           
  300         	36.70930    	-54.88942   	52.11370    	7           
  500         	34.05448    	-22.17957   	48.52899    	12          
  epoch 838 reach nan state
---> try to load best state
              	74.65286    	0.29250     	147.03667   	19               eval state : test_on_restore;
              	34.05448    	-22.17957   	48.52899    	19               eval state : test_on_last_epoch;
---> final result
              	34.05448    	-22.17957   	48.52899    	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	168.76643   	-1.24582    	269.93036   	0           
  10          	86.00018    	-3.33261    	128.17998   	0           
  100         	38.79452    	-61.10358   	56.31758    	4           
  300         	37.01248    	-51.04381   	53.49375    	12          
  500         	29.22624    	-18.22074   	46.67667    	22          
  epoch 792 reach nan state
---> try to load best state
              	31.50712    	-1.84935    	55.93069    	34               eval state : test_on_restore;
              	29.22624    	-18.22074   	46.67667    	34               eval state : test_on_last_epoch;
---> final result
              	29.22624    	-18.22074   	46.67667    	34               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	123.63412   	-1.24570    	198.11273   	0           
  10          	80.38799    	-5.26722    	120.65750   	1           
  100         	38.40081    	-44.16438   	54.99869    	9           
  300         	35.16544    	-28.65977   	50.45485    	27          
  500         	29.14508    	-17.50533   	46.92449    	44          
  epoch 848 reach nan state
---> try to load best state
              	17.04549    	0.41452     	32.85910    	75               eval state : test_on_restore;
              	29.14508    	-17.50533   	46.92449    	75               eval state : test_on_last_epoch;
---> final result
              	17.04549    	0.41452     	32.85910    	75               eval state : final;module_name : GAR;cp_record_file : True;
