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

  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': 32, '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           	0.02180     	-0.35977    	0.03647     	0           
  10          	0.01947     	-0.06583    	0.03293     	0           
  100         	0.00829     	0.78151     	0.01395     	2           
  300         	0.00420     	0.94439     	0.00650     	7           
  500         	0.00379     	0.96277     	0.00559     	11          
---> try to load best state
              	0.00478     	0.81682     	0.00652     	18               eval state : test_on_restore;
              	0.02298     	-5.80454    	0.02804     	18               eval state : test_on_last_epoch;
---> final result              	0.00478     	0.81682     	0.00652     	18               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           	564.97729   	-24.24145   	732.10559   	0           
  10          	501.92130   	-33.57070   	686.93109   	0           
  100         	311.09412   	-4.29918    	454.49292   	1           
  300         	225.26590   	-1.70736    	325.02667   	4           
  500         	185.17952   	-0.95572    	271.16092   	7           
  epoch 793 reach nan state
---> try to load best state
              	170.92290   	-0.78128    	253.33664   	11               eval state : test_on_restore;
              	185.17952   	-0.95572    	271.16092   	11               eval state : test_on_last_epoch;
---> final result
              	170.92290   	-0.78128    	253.33664   	11               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           	443.14734   	-8.77734    	637.22485   	0           
  10          	380.30121   	-11.00186   	561.47589   	0           
  100         	200.05267   	-1.02570    	330.83398   	2           
  300         	128.88338   	-0.12042    	220.18217   	6           
  500         	107.21185   	0.01219     	185.94060   	10          
  epoch 751 reach nan state
---> try to load best state
              	101.80527   	0.03834     	177.91473   	15               eval state : test_on_restore;
              	107.21185   	0.01219     	185.94060   	15               eval state : test_on_last_epoch;
---> final result
              	101.80527   	0.03834     	177.91473   	15               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           	264.62964   	-1.97810    	467.89212   	0           
  10          	222.99319   	-5.76700    	398.43158   	0           
  100         	98.76077    	0.15698     	177.54004   	2           
  300         	58.29878    	0.38366     	114.51716   	8           
  500         	50.17883    	0.39895     	100.72388   	13          
  epoch 761 reach nan state
---> try to load best state
              	66.44995    	0.36237     	132.64171   	21               eval state : test_on_restore;
              	50.17883    	0.39895     	100.72388   	21               eval state : test_on_last_epoch;
---> final result
              	50.17883    	0.39895     	100.72388   	21               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           	235.99413   	-1.39765    	416.53772   	0           
  10          	198.23033   	-5.57867    	351.78882   	0           
  100         	75.94785    	0.01154     	133.36739   	4           
  300         	29.89358    	0.33140     	61.71618    	14          
  500         	27.75384    	0.34286     	60.10887    	24          
  epoch 611 reach nan state
---> try to load best state
              	30.99930    	0.32594     	69.03645    	29               eval state : test_on_restore;
              	27.75384    	0.34286     	60.10887    	29               eval state : test_on_last_epoch;
---> final result
              	27.75384    	0.34286     	60.10887    	29               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': 32, '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           	0.01963     	-0.09295    	0.03130     	0           
  10          	0.01771     	0.10086     	0.02850     	0           
  100         	0.00803     	0.74700     	0.01277     	2           
  300         	0.00431     	0.91673     	0.00684     	6           
  500         	0.00342     	0.95484     	0.00552     	11          
---> try to load best state
              	0.00498     	0.67109     	0.00650     	18               eval state : test_on_restore;
              	0.02921     	-8.17840    	0.03470     	18               eval state : test_on_last_epoch;
---> final result              	0.00498     	0.67109     	0.00650     	18               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           	526.00977   	-22.10749   	782.01630   	0           
  10          	458.05786   	-39.95173   	690.19135   	0           
  100         	248.65495   	-3.40735    	395.25183   	1           
  300         	201.52817   	-1.50935    	367.26471   	4           
  500         	184.79921   	-1.17778    	354.52597   	7           
  epoch 782 reach nan state
---> try to load best state
              	179.15857   	-1.08862    	348.76733   	11               eval state : test_on_restore;
              	184.79921   	-1.17778    	354.52597   	11               eval state : test_on_last_epoch;
---> final result
              	179.15857   	-1.08862    	348.76733   	11               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           	492.66940   	-16.92350   	770.53851   	0           
  10          	428.29111   	-25.92285   	679.80219   	0           
  100         	203.36769   	-2.29694    	348.42722   	2           
  300         	92.44788    	-0.15086    	213.06892   	6           
  500         	66.57466    	0.12616     	162.74606   	9           
  epoch 726 reach nan state
---> try to load best state
              	63.43835    	0.12576     	156.22426   	14               eval state : test_on_restore;
              	66.57466    	0.12616     	162.74606   	14               eval state : test_on_last_epoch;
---> final result
              	63.43835    	0.12576     	156.22426   	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': 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           	313.97110   	-3.34804    	499.59549   	0           
  10          	265.34406   	-8.00488    	427.06931   	0           
  100         	105.36302   	-0.22792    	172.72484   	2           
  300         	40.21194    	0.26694     	77.99479    	8           
  500         	31.25673    	0.35080     	66.46275    	14          
  epoch 675 reach nan state
---> try to load best state
              	31.85267    	0.34083     	67.09103    	19               eval state : test_on_restore;
              	31.25673    	0.35080     	66.46275    	19               eval state : test_on_last_epoch;
---> final result
              	31.25673    	0.35080     	66.46275    	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': 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           	208.06412   	-1.09989    	355.40363   	0           
  10          	177.94632   	-5.01721    	302.75549   	0           
  100         	74.74647    	-0.35346    	123.89857   	4           
  300         	26.65064    	0.34162     	55.13629    	14          
  500         	20.86675    	0.38452     	45.22071    	24          
  epoch 757 reach nan state
---> try to load best state
              	28.73226    	0.32897     	56.47857    	36               eval state : test_on_restore;
              	20.86675    	0.38452     	45.22071    	36               eval state : test_on_last_epoch;
---> final result
              	20.86675    	0.38452     	45.22071    	36               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': 32, '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           	0.02215     	-0.21049    	0.03634     	0           
  10          	0.01988     	0.01874     	0.03315     	0           
  100         	0.00851     	0.76310     	0.01476     	2           
  300         	0.00453     	0.93622     	0.00757     	6           
  500         	0.00347     	0.96511     	0.00571     	10          
---> try to load best state
              	0.00389     	0.84525     	0.00570     	17               eval state : test_on_restore;
              	0.01884     	-7.07914    	0.02279     	17               eval state : test_on_last_epoch;
---> final result              	0.00389     	0.84525     	0.00570     	17               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           	653.58832   	-259.30714  	867.88519   	0           
  10          	565.04810   	-530.19550  	784.58545   	0           
  100         	434.28265   	-32.50427   	542.71710   	1           
  300         	421.87476   	-4.77387    	623.89185   	4           
  500         	348.24826   	-2.98484    	528.58533   	7           
  1000        	404.14993   	-3.94106    	601.32812   	14          
---> try to load best state
              	404.14993   	-3.94106    	601.32812   	14               eval state : test_on_restore;
              	404.14993   	-3.94106    	601.32812   	14               eval state : test_on_last_epoch;
---> final result
              	404.14993   	-3.94106    	601.32812   	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': 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           	486.16580   	-12.83746   	781.68713   	0           
  10          	420.50229   	-20.38405   	696.54694   	0           
  100         	214.31453   	-1.52935    	375.79160   	2           
  300         	152.75587   	-0.34184    	308.34601   	6           
  500         	118.38279   	-0.16631    	246.77425   	9           
  epoch 833 reach nan state
---> try to load best state
              	198.10126   	-0.61326    	386.49554   	16               eval state : test_on_restore;
              	118.38279   	-0.16631    	246.77425   	16               eval state : test_on_last_epoch;
---> final result
              	118.38279   	-0.16631    	246.77425   	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': 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           	378.71591   	-3.82162    	614.33734   	0           
  10          	317.15057   	-7.85966    	525.20514   	0           
  100         	120.49360   	-0.51896    	210.15248   	2           
  300         	50.73749    	0.08806     	117.97624   	8           
  500         	40.99492    	0.10389     	104.47480   	14          
  1000        	54.75574    	0.01220     	127.53342   	27          
---> try to load best state
              	54.75574    	0.01220     	127.53342   	27               eval state : test_on_restore;
              	54.75574    	0.01220     	127.53342   	27               eval state : test_on_last_epoch;
---> final result
              	54.75574    	0.01220     	127.53342   	27               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           	252.48740   	-1.39022    	423.60400   	0           
  10          	211.08936   	-4.30763    	358.95175   	0           
  100         	79.56425    	-0.53422    	141.67216   	4           
  300         	26.20779    	0.21695     	57.00840    	14          
  500         	21.24751    	0.15339     	44.47076    	24          
  epoch 563 reach nan state
---> try to load best state
              	20.28446    	0.20904     	43.16359    	27               eval state : test_on_restore;
              	21.24751    	0.15339     	44.47076    	27               eval state : test_on_last_epoch;
---> final result
              	20.28446    	0.20904     	43.16359    	27               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': 32, '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           	0.02011     	-0.29680    	0.03417     	0           
  10          	0.01815     	-0.01866    	0.03055     	0           
  100         	0.00816     	0.75462     	0.01301     	2           
  300         	0.00417     	0.93716     	0.00684     	6           
  500         	0.00352     	0.96173     	0.00572     	11          
---> try to load best state
              	0.00566     	0.49820     	0.00729     	18               eval state : test_on_restore;
              	0.03136     	-8.77989    	0.03702     	18               eval state : test_on_last_epoch;
---> final result              	0.00566     	0.49820     	0.00729     	18               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           	569.87152   	-77.23260   	851.04370   	0           
  10          	528.76324   	-105.97898  	765.33038   	0           
  100         	352.05099   	-24.80786   	503.31668   	1           
  300         	359.05896   	-19.04724   	566.54871   	5           
  500         	405.02255   	-8.63712    	675.37714   	7           
  epoch 851 reach nan state
---> try to load best state
              	471.65744   	-12.26024   	759.14246   	12               eval state : test_on_restore;
              	405.02255   	-8.63712    	675.37714   	12               eval state : test_on_last_epoch;
---> final result
              	405.02255   	-8.63712    	675.37714   	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': 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           	465.86005   	-19.45515   	722.40594   	0           
  10          	411.47321   	-26.04003   	624.01599   	0           
  100         	181.03105   	-1.40110    	320.60840   	2           
  300         	120.32825   	-0.14298    	239.61139   	5           
  500         	101.61914   	0.01156     	208.13562   	9           
  epoch 658 reach nan state
---> try to load best state
              	97.14926    	0.04266     	201.61790   	12               eval state : test_on_restore;
              	101.61914   	0.01156     	208.13562   	13               eval state : test_on_last_epoch;
---> final result
              	97.14926    	0.04266     	201.61790   	13               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           	312.37619   	-3.83972    	516.66766   	0           
  10          	265.40286   	-6.35314    	438.43326   	0           
  100         	107.40055   	0.01828     	188.37560   	3           
  300         	57.96611    	0.26179     	126.30256   	8           
  500         	48.71148    	0.30457     	113.83922   	15          
  epoch 609 reach nan state
---> try to load best state
              	47.42687    	0.31520     	111.60628   	18               eval state : test_on_restore;
              	48.71148    	0.30457     	113.83922   	18               eval state : test_on_last_epoch;
---> final result
              	47.42687    	0.31520     	111.60628   	18               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           	213.18781   	-1.38173    	392.07144   	0           
  10          	183.81026   	-4.96286    	329.14493   	0           
  100         	76.67036    	-1.11392    	130.30669   	5           
  300         	29.79650    	0.28224     	62.13811    	15          
  500         	22.67653    	0.36051     	51.14128    	25          
  epoch 632 reach nan state
---> try to load best state
              	26.56832    	-2.74727    	54.66274    	31               eval state : test_on_restore;
              	22.67653    	0.36051     	51.14128    	31               eval state : test_on_last_epoch;
---> final result
              	22.67653    	0.36051     	51.14128    	31               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': 32, '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           	0.01514     	0.43667     	0.02511     	0           
  10          	0.01388     	0.53967     	0.02295     	0           
  100         	0.00721     	0.85321     	0.01122     	2           
  300         	0.00419     	0.95212     	0.00649     	6           
  500         	0.00361     	0.96749     	0.00561     	11          
---> try to load best state
              	0.00534     	0.65114     	0.00723     	18               eval state : test_on_restore;
              	0.02824     	-6.38641    	0.03354     	18               eval state : test_on_last_epoch;
---> final result              	0.00534     	0.65114     	0.00723     	18               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           	605.19751   	-773.97005  	879.38947   	0           
  10          	538.34729   	-1336.26405 	790.07269   	0           
  100         	389.32431   	-62.66556   	543.47876   	1           
  300         	436.48795   	-11.19180   	686.86005   	4           
  500         	430.36765   	-8.66572    	681.07434   	7           
  epoch 876 reach nan state
---> try to load best state
              	473.68835   	-12.22867   	737.67847   	12               eval state : test_on_restore;
              	430.36765   	-8.66572    	681.07434   	13               eval state : test_on_last_epoch;
---> final result
              	430.36765   	-8.66572    	681.07434   	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': 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           	386.70328   	-10.79094   	590.07800   	0           
  10          	326.56146   	-14.49162   	505.60068   	0           
  100         	120.75826   	-0.53626    	202.39641   	2           
  300         	70.23068    	0.15030     	137.18477   	6           
  500         	62.67173    	0.20969     	127.76794   	10          
  epoch 633 reach nan state
---> try to load best state
              	61.58014    	0.22239     	126.67453   	12               eval state : test_on_restore;
              	62.67173    	0.20969     	127.76794   	13               eval state : test_on_last_epoch;
---> final result
              	61.58014    	0.22239     	126.67453   	13               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           	281.17841   	-2.74466    	474.17749   	0           
  10          	233.88000   	-7.26302    	402.31256   	0           
  100         	98.69617    	0.19140     	163.94615   	3           
  300         	55.06533    	0.35033     	112.51148   	8           
  500         	43.83270    	0.37901     	96.87205    	14          
  epoch 717 reach nan state
---> try to load best state
              	53.74315    	0.35366     	112.67613   	20               eval state : test_on_restore;
              	43.83270    	0.37901     	96.87205    	20               eval state : test_on_last_epoch;
---> final result
              	43.83270    	0.37901     	96.87205    	20               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           	164.72777   	-0.54356    	286.77875   	0           
  10          	143.15865   	-4.55639    	243.90739   	0           
  100         	64.65802    	0.05501     	107.46745   	4           
  300         	27.15397    	0.41273     	57.09523    	14          
  500         	22.22137    	0.43214     	47.56744    	24          
  epoch 681 reach nan state
---> try to load best state
              	25.66823    	0.42103     	52.97606    	32               eval state : test_on_restore;
              	22.22137    	0.43214     	47.56744    	32               eval state : test_on_last_epoch;
---> final result
              	22.22137    	0.43214     	47.56744    	32               eval state : final;module_name : GAR;cp_record_file : True;
