Add optimization results and update training scripts for width optimization

Code/data/width_optimization/2W/rbf_FEMdata_B29_H30.csv

 tw1,tw2,Exymax_tw1,Exymax_tw2,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFMmax_frame
 15.22,18.46,0.0392701,0.0444714,0.1191054,73.5564,63.4273,88.8872
-12.74,12.61,0.040121,0.0783297,0.1249457,72.9956,112.404,93.351
-18.03,11.3,0.0227754,0.0992588,0.1671307,35.5244,145.9917,115.5385
-21.19,19.38,0.0201374,0.0506448,0.151535,40.3906,70.257,102.5168
+12.74,12.61,0.0401210,0.0783297,0.1249457,72.9956,112.404,93.3510
+18.03,11.30,0.0227754,0.0992588,0.1671307,35.5244,145.9917,115.5385
+21.19,19.38,0.0201374,0.0506448,0.1515350,40.3906,70.2570,102.5168
 9.78,15.96,0.0710369,0.0433733,0.1167672,143.7781,74.3999,80.5918
-18.64,13.8,0.024436,0.0814959,0.1546975,36.5113,106.3158,107.9247
-13.46,20.99,0.053643,0.0293971,0.1016962,105.4959,49.82,83.5084
-8.58,8.88,0.0632531,0.1042515,0.130283,123.1308,165.6866,86.976
-12.34,14.33,0.0463968,0.0639672,0.1158917,91.3678,95.3181,87.7947
+18.64,13.8,0.0244360,0.0814959,0.1546975,36.5113,106.3158,107.9247
+13.46,20.99,0.0536430,0.0293971,0.1016962,105.4959,49.8200,83.5084
+8.58,8.88,0.0632531,0.1042515,0.1302830,123.1308,165.6866,86.9760
+12.34,14.34,0.0461157,0.0635088,0.1160707,90.9982,94.5993,87.6823

Code/data/width_optimization/2W/rbf_FEMdata_B34_H30.csv

@@ -2,9 +2,9 @@ tw1,tw2,Exymax_tw1,Exymax_tw2,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFMmax_frame
 15.22,18.46,0.0510256,0.0539798,0.1449565,100.9598,83.4741,99.2565
 12.74,12.61,0.0625263,0.0940645,0.1555176,93.4107,143.0323,105.1522
 18.03,11.3,0.0295755,0.1281388,0.2320882,41.7941,188.8763,133.5331
-21.19,19.38,0.0349424,0.0675929,0.1833217,50.9109,88.895,118.7431
+21.19,19.38,0.0349424,0.0675929,0.1833217,50.9109,88.8950,118.7431
 9.78,15.96,0.0989222,0.0506776,0.1337613,180.2326,82.7747,91.5803
 18.64,13.8,0.0302939,0.1027169,0.1913927,44.3285,142.8628,125.6498
-13.46,20.99,0.0686057,0.033276,0.1173378,138.993,60.5905,89.5536
+13.46,20.99,0.0686057,0.0332760,0.1173378,138.9930,60.5905,89.5536
 8.58,8.88,0.1085222,0.1382075,0.1669545,146.7474,192.9294,94.6607
-16.41,19.87,0.0509794,0.0499679,0.1471728,95.3688,76.9264,102.2025
+15.50,20.00,0.0521125,0.0454009,0.1396136,105.8219,72.7413,98.4961

Code/data/width_optimization/3W/rbf_FEMdata_B29_H45.csv

 tw1,tw2,tw3,Exymax_tw1,Exymax_tw2,Exymax_tw3,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFMmax_tw3,TFMmax_frame
-8.93,10.69,11.92,0.0246997,0.0417768,0.0464886,0.1013018,42.8515,55.874,68.8582,72.3628
-14.65,7.11,11.38,0.0027074,0.0739921,0.0456774,0.1320175,3.2259,113.0944,74.3599,81.734
+8.93,10.69,11.92,0.0246997,0.0417768,0.0464886,0.1013018,42.8515,55.8740,68.8582,72.3628
+14.65,7.11,11.38,0.0027074,0.0739921,0.0456774,0.1320175,3.2259,113.0944,74.3599,81.7340
 6.29,6.52,9.56,0.0400088,0.0663047,0.0487848,0.1169582,74.9831,119.2527,87.0856,69.9576
-4.58,8.02,12.59,0.0837647,0.0655233,0.030548,0.0898686,157.3271,109.3948,50.2318,58.5561
-10.95,6.12,10.09,0.010129,0.0826896,0.0504054,0.1372914,11.6665,133.1484,87.2615,81.953
-13.03,8.96,14.9,0.0072895,0.060993,0.0302428,0.1157139,8.0061,91.7049,50.4625,79.2896
-15.45,11.1,6.67,0.0024481,0.0291431,0.1215571,0.2017346,0.6759,50.175,190.3621,126.9695
-14.25,13.56,5.6,0.0020784,0.0282992,0.1565546,0.2872795,1.0843,44.4171,247.7598,155.7495
-5.22,14.05,8.76,0.0602599,0.0185185,0.061292,0.1309323,125.8713,32.7268,111.9502,90.7167
-12.69,4.24,7.49,0.0093316,0.1332601,0.1120888,0.1370617,6.2487,206.661,123.4632,79.5508
-7.82,9.84,14.42,0.0344974,0.0463493,0.0297139,0.0968073,64.2934,77.0343,49.795,67.3378
-5.72,11.78,13.37,0.058668,0.0349843,0.031118,0.0795838,121.0071,55.9826,53.3913,57.7322
+4.58,8.02,12.59,0.0837647,0.0655233,0.0305480,0.0898686,157.3271,109.3948,50.2318,58.5561
+10.95,6.12,10.09,0.0101290,0.0826896,0.0504054,0.1372914,11.6665,133.1484,87.2615,81.9530
+13.03,8.96,14.90,0.0072895,0.0609930,0.0302428,0.1157139,8.0061,91.7049,50.4625,79.2896
+15.45,11.10,6.67,0.0024481,0.0291431,0.1215571,0.2017346,0.6759,50.1750,190.3621,126.9695
+14.25,13.56,5.60,0.0020784,0.0282992,0.1565546,0.2872795,1.0843,44.4171,247.7598,155.7495
+5.22,14.05,8.76,0.0602599,0.0185185,0.0612920,0.1309323,125.8713,32.7268,111.9502,90.7167
+12.69,4.24,7.49,0.0093316,0.1332601,0.1120888,0.1370617,6.2487,206.6610,123.4632,79.5508
+7.82,9.84,14.42,0.0344974,0.0463493,0.0297139,0.0968073,64.2934,77.0343,49.7950,67.3378
+5.72,11.78,13.37,0.0586680,0.0349843,0.0311180,0.0795838,121.0071,55.9826,53.3913,57.7322
 10.54,4.85,8.21,0.0178929,0.1071379,0.0903543,0.1374885,13.2657,171.3972,112.4605,79.8828
-7.07,15.26,4.16,0.0199412,0.030974,0.1744638,0.2911736,31.3565,37.3897,285.5494,166.6332
-11.94,14.8,15.93,0.0162564,0.0280608,0.0349907,0.0939799,25.3627,48.0349,62.0809,72.4997
-9.81,12.65,4.96,0.0092622,0.0285342,0.1633582,0.2867144,11.1527,44.1879,260.5004,156.512
-5.94,8.39,9.27,0.0462962,0.0447716,0.053344,0.1008605,89.2104,81.7976,85.8065,69.9243
-5.68,7.97,9,0.0493762,0.0478416,0.0536288,0.1003415,94.4223,88.5502,88.8948,68.5487
+7.07,15.26,4.16,0.0199412,0.0309740,0.1744638,0.2911736,31.3565,37.3897,285.5494,166.6332
+11.94,14.80,15.93,0.0162564,0.0280608,0.0349907,0.0939799,25.3627,48.0349,62.0809,72.4997
+9.81,12.65,4.96,0.0092622,0.0285342,0.1633582,0.2867144,11.1527,44.1879,260.5004,156.5120
+5.94,8.38,9.28,0.0463239,0.0448421,0.0532387,0.1007058,89.2364,82.0068,85.7161,69.8185
+5.68,7.97,9.00,0.0493762,0.0478416,0.0536288,0.1003415,94.4223,88.5502,88.8948,68.5487

Code/data/width_optimization/3W/rbf_FEMdata_B34_H45.csv

 tw1,tw2,tw3,Exymax_tw1,Exymax_tw2,Exymax_tw3,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFMmax_tw3,TFMmax_frame
-8.93,10.69,11.92,0.0314285,0.036188,0.0500572,0.096886,60.3168,73.0743,75.173,70.8353
+8.93,10.69,11.92,0.0314285,0.0361880,0.0500572,0.0968860,60.3168,73.0743,75.1730,70.8353
 14.65,7.11,11.38,0.0041545,0.0770628,0.0446173,0.1298872,5.7242,140.3068,76.6369,85.1589
-6.29,6.52,9.56,0.042533,0.0678026,0.0470426,0.1058771,91.4702,145.2878,86.7668,68.0555
-4.58,8.02,12.59,0.1047867,0.0870891,0.0314758,0.083459,202.6256,124.2751,48.3992,57.2506
+6.29,6.52,9.56,0.0425330,0.0678026,0.0470426,0.1058771,91.4702,145.2878,86.7668,68.0555
+4.58,8.02,12.59,0.1047867,0.0870891,0.0314758,0.0834590,202.6256,124.2751,48.3992,57.2506
 10.95,6.12,10.09,0.0169834,0.0839863,0.0570973,0.1329752,18.5951,160.5635,88.2842,83.5555
-13.03,8.96,14.9,0.0109994,0.0652393,0.0298515,0.116083,14.5435,117.7404,55.5012,82.8776
-15.45,11.1,6.67,0.001837,0.0306984,0.1385195,0.2498688,1.1291,55.9504,197.4358,125.0097
-14.25,13.56,5.6,0.0018503,0.032009,0.1733572,0.3333523,0.8401,48.5556,257.8822,152.0872
+13.03,8.96,14.90,0.0109994,0.0652393,0.0298515,0.1160830,14.5435,117.7404,55.5012,82.8776
+15.45,11.10,6.67,0.0018370,0.0306984,0.1385195,0.2498688,1.1291,55.9504,197.4358,125.0097
+14.25,13.56,5.60,0.0018503,0.0320090,0.1733572,0.3333523,0.8401,48.5556,257.8822,152.0872
 5.22,14.05,8.76,0.0767186,0.0184562,0.0625321,0.1078929,170.6635,33.4413,105.7267,80.4824
 12.69,4.24,7.49,0.0087924,0.1325386,0.1181118,0.1384105,9.2216,229.5643,119.7812,81.6469
-7.82,9.84,14.42,0.042177,0.047482,0.0297936,0.0934205,88.3429,98.3484,53.4841,67.0666
-5.72,11.78,13.37,0.0772789,0.0353789,0.0293972,0.0719482,166.3376,67.8034,55.0039,54.502
+7.82,9.84,14.42,0.0421770,0.0474820,0.0297936,0.0934205,88.3429,98.3484,53.4841,67.0666
+5.72,11.78,13.37,0.0772789,0.0353789,0.0293972,0.0719482,166.3376,67.8034,55.0039,54.5020
 10.54,4.85,8.21,0.0161377,0.1107135,0.0903302,0.1383831,18.5385,195.3177,110.0121,81.7428
-7.07,15.26,4.16,0.0205172,0.0282205,0.1826803,0.28507,40.8943,38.3046,285.025,149.7966
-11.94,14.8,15.93,0.0239298,0.0817664,0.0338733,0.1028892,42.4723,87.8776,64.2093,69.8335
-9.81,12.65,4.96,0.0089586,0.032101,0.1749998,0.3156683,13.1979,47.4165,266.644,147.5593
-7.2,9.27,9.81,0.0394869,0.0424188,0.056382,0.092518,82.3909,85.2522,90.2833,70.8243
+7.07,15.26,4.16,0.0205172,0.0282205,0.1826803,0.2850700,40.8943,38.3046,285.0250,149.7966
+11.94,14.80,15.93,0.0239298,0.0817664,0.0338733,0.1028892,42.4723,87.8776,64.2093,69.8335
+9.81,12.65,4.96,0.0089586,0.0321010,0.1749998,0.3156683,13.1979,47.4165,266.6440,147.5593
+7.21,9.27,9.82,0.0389181,0.0460074,0.0552465,0.0930625,82.2030,85.0636,90.3346,70.8627

Code/models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/cv_summary_per_output_B29_H30.csv

 output,best_model,cv_rmse,std_rmse,cv_rmse_dispersion,cv_mae,cv_r2,BEST_PARAMS,model_path,train_time_sec,gpr_kernel,selected_by
-exymax_tw2,SVR,0.003944696026097462,0.0033326552556185705,0.8448446302504097,0.003944696026097462,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_exymax_tw2.joblib,246.08,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-exymax_tw1,SVR,0.00396466323858515,0.002615136403582738,0.65961123207932,0.00396466323858515,,"{""svr__C"": 6378.353975271747, ""svr__epsilon"": 0.00017612867304089586, ""svr__gamma"": 0.0001299730411455674}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_exymax_tw1.joblib,331.85,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_frame,SVR,1.8448767818749428,1.8179078246426847,0.985381702725507,1.8448767818749428,,"{""svr__C"": 872.0933090674746, ""svr__epsilon"": 0.008696229680912925, ""svr__gamma"": 0.014144889231893125}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_tfmmax_frame.joblib,325.87,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_tw1,SVR,5.999344323594288,7.75478808008392,1.2926059352162542,5.999344323594288,,"{""svr__C"": 9473.450684118796, ""svr__epsilon"": 0.03698445205342322, ""svr__gamma"": 0.017296157005375526}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_tfmmax_tw1.joblib,225.04,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_tw2,FlexibleMLP,10.397110371014389,7.944933680445426,0.7641482485936409,10.397110371014389,,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 0.0013843697688682688, ""mlp__learning_rate_init"": 0.0012259398541783753, ""mlp__n_layers"": 1, ""mlp__n_neurons"": 68}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_tfmmax_tw2.joblib,222.32,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw2,SVR,0.0038124872313874763,0.003340839390048544,0.8762886764692756,0.0038124872313874763,,"{""svr__C"": 9978.136366417832, ""svr__epsilon"": 0.00033424143012809605, ""svr__gamma"": 0.001381610799038026}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_exymax_tw2.joblib,355.19,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw1,SVR,0.004061647396259379,0.00242300813481289,0.5965579722760738,0.004061647396259379,,"{""svr__C"": 9561.792183843145, ""svr__epsilon"": 0.0003108623718211416, ""svr__gamma"": 0.00010040004983034561}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_exymax_tw1.joblib,305.52,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_frame,SVR,1.8473828812332656,1.8543467263702917,1.0037695732745868,1.8473828812332656,,"{""svr__C"": 2619.206360822925, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.008488619625011348}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_tfmmax_frame.joblib,2609.71,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw1,SVR,5.76420278175457,7.84041278534167,1.3601903129013655,5.76420278175457,,"{""svr__C"": 10000.0, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.01599115973912198}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_tfmmax_tw1.joblib,308.43,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw2,SVR,8.5556547519197,5.490513935936841,0.641740941534006,8.5556547519197,,"{""svr__C"": 7123.257292646505, ""svr__epsilon"": 0.1, ""svr__gamma"": 0.012177597403964988}",../../models/width_optimization/2W/ml_models/per_output_models_B29_H30/it0/best_model_tfmmax_tw2.joblib,384.47,,lowest_cv_rmse_dispersion_within_5pct_rmse_band

Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/cv_summary_per_output_B34_H30.csv

 output,best_model,cv_rmse,std_rmse,cv_rmse_dispersion,cv_mae,cv_r2,BEST_PARAMS,model_path,train_time_sec,gpr_kernel,selected_by
-exymax_tw1,SVR,0.0008167425561569747,0.0006767801130116837,0.8286333409589217,0.0008167425561569747,,"{""svr__C"": 4442.8726934917495, ""svr__epsilon"": 0.00012917724146922354, ""svr__gamma"": 0.001504160883442332}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_exymax_tw1.joblib,277.24,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-exymax_tw2,SVR,0.006136161710932672,0.005656049157920998,0.9217568611080983,0.006136161710932672,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_exymax_tw2.joblib,255.91,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_frame,SVR,3.715373578591194,2.334838394610003,0.6284262794093868,3.715373578591194,,"{""svr__C"": 5216.1520937153, ""svr__epsilon"": 0.013648761321634565, ""svr__gamma"": 0.015037530613724375}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_tfmmax_frame.joblib,370.56,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_tw1,SVR,6.741286194591464,8.748451173110228,1.2977421400873193,6.741286194591464,,"{""svr__C"": 2768.7251482836446, ""svr__epsilon"": 0.023317529207347875, ""svr__gamma"": 0.028189099645040516}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_tfmmax_tw1.joblib,232.73,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
-tfmmax_tw2,GaussianProcess,11.739301648902373,8.161087859526862,0.6951936412921058,11.739301648902373,,"{""gpr__amplitude"": 1.7905875875772057, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 1.0810730734949554, ""gpr__n_restarts_optimizer"": 9, ""gpr__noise"": 9.47686335625517e-05, ""gpr__rq_alpha"": 1.4204189514600063}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_tfmmax_tw2.joblib,279.57,"3.2**2 * Matern(length_scale=6, nu=2.5) + WhiteKernel(noise_level=0.00704)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw1,SVR,0.0009391988885412684,0.0008408212014288188,0.8952536163397228,0.0009391988885412684,,"{""svr__C"": 1145.053088286465, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.00290506172148416}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_exymax_tw1.joblib,289.88,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw2,SVR,0.006029466121502341,0.005518640222413557,0.9152784195491087,0.006029466121502341,,"{""svr__C"": 10000.0, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.0034683155294780777}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_exymax_tw2.joblib,262.41,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_frame,SVR,3.2580037673327116,2.5119569360841667,0.7710110593704671,3.2580037673327116,,"{""svr__C"": 10000.0, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.013154596167883838}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_tfmmax_frame.joblib,657.42,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw1,SVR,7.183119170413128,8.309840082499269,1.156856775636835,7.183119170413128,,"{""svr__C"": 8486.611629527324, ""svr__epsilon"": 0.0001, ""svr__gamma"": 0.01016134780490442}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_tfmmax_tw1.joblib,349.24,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw2,SVR,10.383152021375174,7.182550943352127,0.6917505328406864,10.383152021375174,,"{""svr__C"": 10000.0, ""svr__epsilon"": 0.1, ""svr__gamma"": 0.014504091099759808}",../../models/width_optimization/2W/ml_models/per_output_models_B34_H30/it0/best_model_tfmmax_tw2.joblib,785.79,,lowest_cv_rmse_dispersion_within_5pct_rmse_band

Code/models/width_optimization/2W/rbf_models/per_output_models_B29_H30/it1/rbf_LOO_results.csv

+output,LOO_RMSE,LOO_MAE,LOO_R2,LOO_RMSE_STD,LOO_RMSE_CV,LOO_MAE_STD,LOO_SQERR_STD
+exymax_tw1,0.008150172980533048,0.005360493131542719,0.7712544324577968,0.006139253456186208,1.1452777394790479,0.006139253456186208,0.00013861598777410185
+exymax_tw2,0.008572862763522579,0.005833395187413924,0.8801428734246546,0.0062821554063591965,1.0769295075213683,0.0062821554063591965,0.00013899408657731815
+tfmmax_tw1,10.680222740245128,6.855102191400704,0.9160387068488547,8.18991646640565,1.194718362722497,8.18991646640565,247.11727801028164
+tfmmax_tw2,13.395392683237962,11.974619364439299,0.8656018338209511,6.003751844883375,0.501373084368139,6.003751844883375,153.4530221508567
+tfmmax_frame,12.261032928851478,7.414418742924263,-0.19112332444358215,9.765209838347236,1.3170566941159592,9.765209838347236,305.4757083982322

Code/models/width_optimization/2W/rbf_models/per_output_models_B34_H30/it1/rbf_LOO_results.csv

+output,LOO_RMSE,LOO_MAE,LOO_R2,LOO_RMSE_STD,LOO_RMSE_CV,LOO_MAE_STD,LOO_SQERR_STD
+exymax_tw1,0.0067951309608360955,0.005468506441657539,0.9363332594154856,0.004033514853383247,0.7375898513453452,0.004033514853383247,4.5648407965412274e-05
+exymax_tw2,0.010331124979124655,0.007661182112240156,0.9163717254906855,0.006930976264378983,0.9046875746897421,0.006930976264378983,0.00013596775440067368
+tfmmax_tw1,16.65690465603714,11.796620331132795,0.8693803364842014,11.75977131935039,0.9968763077264464,11.75977131935039,394.99021740557555
+tfmmax_tw2,13.844677984260237,10.360091395134175,0.915862399267476,9.183877981132314,0.8864668882598571,9.183877981132314,261.01543432786775
+tfmmax_frame,16.27367436030384,9.198502506096437,-0.18269815591915717,13.42460535101677,1.4594337874147911,13.42460535101677,621.8121617330642

Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it1/rbf_LOO_results.csv

+output,LOO_RMSE,LOO_MAE,LOO_R2,LOO_RMSE_STD,LOO_RMSE_CV,LOO_MAE_STD,LOO_SQERR_STD
+exymax_tw1,0.009190462825822386,0.006555357477294431,0.8453857278703277,0.006441420285752449,0.98261922527694,0.006441420285752449,0.00017181266353825323
+exymax_tw2,0.009561989511240931,0.00780680764044274,0.9010182687700412,0.0055213583363341355,0.7072491843824921,0.0055213583363341355,0.00010675387888540671
+exymax_tw3,0.012254378933659786,0.008847059618380462,0.9382031312389641,0.008479347802664218,0.958436833074766,0.008479347802664218,0.0002587402983269403
+tfmmax_tw1,15.60593832003407,9.246810431239657,0.897723967277053,12.571468000890956,1.3595464181269676,12.571468000890956,473.29935967123816
+tfmmax_tw2,10.654495593789711,8.313696872079788,0.9515075314778572,6.663386577203453,0.8014950123550166,6.663386577203453,171.8915554159779
+tfmmax_tw3,15.147861114739676,9.998707194184908,0.9605655290927848,11.37908391727547,1.138055520206989,11.37908391727547,410.83801514453637
+tfmmax_frame,7.270920660929447,5.176765850734426,0.9558023431350221,5.105622644124867,0.9862572098756474,5.105622644124867,119.43968264760818

Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it1/rbf_LOO_results.csv

+output,LOO_RMSE,LOO_MAE,LOO_R2,LOO_RMSE_STD,LOO_RMSE_CV,LOO_MAE_STD,LOO_SQERR_STD
+exymax_tw1,0.01271109892847045,0.008746127304020301,0.8089139553071438,0.009223735314460907,1.0546079417596705,0.009223735314460907,0.00032992539272167014
+exymax_tw2,0.017087099155815577,0.01180245278726027,0.7023400693902224,0.012356013344326143,1.0469021623762302,0.012356013344326143,0.0005867726178967102
+exymax_tw3,0.012853141734783946,0.009296509310458332,0.9438488063649062,0.008875706568775607,0.9547354036197938,0.008875706568775607,0.0002671563103569735
+tfmmax_tw1,21.925609131135992,13.693767616424415,0.8780819688832557,17.123465310465633,1.2504568348251808,17.123465310465633,867.2586286515489
+tfmmax_tw2,11.658665963212655,8.539220986273767,0.955307036290955,7.937644297230441,0.9295513384639745,7.937644297230441,232.8138565285835
+tfmmax_tw3,17.170461532849675,11.112825367663884,0.9508512989207835,13.089303327483725,1.1778555762759486,13.089303327483725,569.9926878293359
+tfmmax_frame,7.418295621994835,5.177950812310913,0.9442926024113952,5.312243906354122,1.025935567739253,5.312243906354122,129.21998015922588

Code/reports/width_optimization/2W/ml_optimization/it0/optimization_results_2W_B29_H30_TFD_W90.csv

@@ -4,11 +4,11 @@ Configuration_B,29.0
 Configuration_H,30.0
 Configuration_TFD_W,90.0
 Iteration,0.0
-tw1_optimal,12.665567904044316
-tw2_optimal,14.703450572353482
-Objective_score,-1.689954500580596e-06
-Exy_tw1,0.05013423656854821
-Exy_tw2,0.06146949165963539
-TFM_tw1,90.00000001387838
-TFM_tw2,90.00000002387473
-TFM_frame,89.76113477601143
+tw1_optimal,12.602604556600495
+tw2_optimal,14.642223218431933
+Objective_score,-1.703314522821794e-06
+Exy_tw1,0.05047722848321312
+Exy_tw2,0.06182536900480995
+TFM_tw1,90.000000021604
+TFM_tw2,90.00000001502372
+TFM_frame,89.6517557912324

Code/reports/width_optimization/2W/ml_optimization/it0/optimization_results_2W_B34_H30_TFD_W90.csv

@@ -4,11 +4,11 @@ Configuration_B,34.0
 Configuration_H,30.0
 Configuration_TFD_W,90.0
 Iteration,0.0
-tw1_optimal,15.644808251437285
+tw1_optimal,15.694454894049716
 tw2_optimal,20.0
-Objective_score,492.56724388692203
-Exy_tw1,0.05093899889255882
-Exy_tw2,0.04702770620592467
-TFM_tw1,103.73892265716938
-TFM_tw2,73.09918025112492
-TFM_frame,96.59550068680014
+Objective_score,541.9474403032106
+Exy_tw1,0.05065454046456508
+Exy_tw2,0.04732158887659077
+TFM_tw1,105.29400198067538
+TFM_tw2,72.51250951714076
+TFM_frame,96.62331405344696

Code/reports/width_optimization/2W/rbf_optimization/it1/optimization_results_2W_B29_H30_TFD_W90.csv

+Parameter,Value
+Configuration_W,2.0
+Configuration_B,29.0
+Configuration_H,30.0
+Configuration_TFD_W,90.0
+Iteration,1.0
+tw1_optimal,12.684831662276643
+tw2_optimal,14.905421089765571
+Objective_score,-1.5458169860709181e-06
+Exy_tw1,0.04531408098561937
+Exy_tw2,0.06034192767159404
+TFM_tw1,90.0000000200485
+TFM_tw2,90.0000000392817
+TFM_frame,87.5326351207742

Code/reports/width_optimization/2W/rbf_optimization/it1/optimization_results_2W_B34_H30_TFD_W90.csv

+Parameter,Value
+Configuration_W,2.0
+Configuration_B,34.0
+Configuration_H,30.0
+Configuration_TFD_W,90.0
+Iteration,1.0
+tw1_optimal,14.799811569069801
+tw2_optimal,18.930435799107368
+Objective_score,700.7361022316106
+Exy_tw1,0.05458329763144634
+Exy_tw2,0.049017179174410265
+TFM_tw1,109.56930931180048
+TFM_tw2,78.18164265531189
+TFM_frame,96.73836958480248

Code/reports/width_optimization/3W/rbf_optimization/it1/optimization_results_3W_B29_H45_TFD_W90.csv

+Parameter,Value
+Configuration_W,3.0
+Configuration_B,29.0
+Configuration_H,45.0
+Configuration_TFD_W,90.0
+Iteration,1.0
+tw1_optimal,5.685399832978042
+tw2_optimal,7.9673473591210415
+tw3_optimal,9.016029941592292
+Objective_score,-1.1929643499088454e-06
+Exy_tw1,0.0467694639666424
+Exy_tw2,0.048545210631678604
+Exy_tw3,0.05550553027362139
+TFM_tw1,90.00000001065337
+TFM_tw2,90.00000001735125
+TFM_tw3,90.00000001255168
+TFM_frame,70.48753052656542

Code/reports/width_optimization/3W/rbf_optimization/it1/optimization_results_3W_B34_H45_TFD_W90.csv

+Parameter,Value
+Configuration_W,3.0
+Configuration_B,34.0
+Configuration_H,45.0
+Configuration_TFD_W,90.0
+Iteration,1.0
+tw1_optimal,6.81086626084703
+tw2_optimal,9.021258980648522
+tw3_optimal,9.654473348128397
+Objective_score,-1.5352001974814094e-06
+Exy_tw1,0.041897196711776205
+Exy_tw2,0.04824066391668254
+Exy_tw3,0.05385046859908042
+TFM_tw1,90.00000001106143
+TFM_tw2,90.0000000278992
+TFM_tw3,90.00000001853854
+TFM_frame,69.62930961050895

Code/src/width_optimization/ml_surrogate_train.py

@@ -134,13 +134,13 @@ def get_adaptive_cv(n_samples: int):
     Notes
     -----
     - N <= 20: Leave-One-Out
-    - 21 <= N <= 80: RepeatedKFold(5x5)
+    - 21 <= N <= 80: RepeatedKFold(4x5)
     - N > 80: KFold(5)
     """
     if n_samples <= 20:
         return LeaveOneOut()
     if n_samples <= 80:
-        return RepeatedKFold(n_splits=5, n_repeats=5, random_state=42)
+        return RepeatedKFold(n_splits=4, n_repeats=5, random_state=42)
     return KFold(n_splits=5, shuffle=True, random_state=42)
 
 
@@ -547,7 +547,7 @@ def main():
                 cv=cv,
                 n_jobs=-1,
                 random_state=42,
-                optimizer_kwargs={"base_estimator": "RF"},
+                # optimizer_kwargs={"base_estimator": "RF"},
                 return_train_score=False,
                 verbose=0,
             )

Code/src/width_optimization/run_rbf.sh

 #!/bin/bash
-##python rbf_surrogate_train.py --W 2 --B 29 --it 0
-##python rbf_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 0
-##python rbf_surrogate_train.py --W 2 --B 34 --it 0
-##python rbf_optimization_de.py --W 2 --B 34 --TFD_W 90 --it 0
-##python rbf_surrogate_train.py --W 3 --B 29 --it 0
-##python rbf_optimization_de.py --W 3 --B 29 --TFD_W 90 --it 0
-##python rbf_surrogate_train.py --W 3 --B 34 --it 0
-##python rbf_optimization_de.py --W 3 --B 34 --TFD_W 90 --it 0
+python rbf_surrogate_train.py --W 2 --B 29 --it 1
+python rbf_optimization_de.py --W 2 --B 29 --TFD_W 90 --it 1
+python rbf_surrogate_train.py --W 2 --B 34 --it 1
+python rbf_optimization_de.py --W 2 --B 34 --TFD_W 90 --it 1
+python rbf_surrogate_train.py --W 3 --B 29 --it 1
+python rbf_optimization_de.py --W 3 --B 29 --TFD_W 90 --it 1
+python rbf_surrogate_train.py --W 3 --B 34 --it 1
+python rbf_optimization_de.py --W 3 --B 34 --TFD_W 90 --it 1
 ##python rbf_surrogate_train.py --W 5 --B 34 --it 2
 ##python rbf_optimization_de.py --W 5 --B 34 --TFD_W 90 --it 2
 

Manuscript/wileyNJD-AMA.bib

@@ -584,8 +584,23 @@ steel from coupon test results available. First, the theory of metal plasticity
   doi        = {10.1016/j.envsoft.2017.03.010},
   keywords   = {Design of computer experiments, Monte Carlo simulation, Optimal Latin hypercube sampling, Sensitivity analysis, Sequential sampling, Uncertainty analysis},
   shorttitle = {Progressive {Latin} {Hypercube} {Sampling}},
-  url        = {https://www.sciencedirect.com/science/article/pii/S1364815216305096},
   urldate    = {2026-05-06},
 }
 
+@InProceedings{Snoek2012,
+  author    = {Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P.},
+  booktitle = {Proceedings of the 26th {International} {Conference} on {Neural} {Information} {Processing} {Systems} - {Volume} 2},
+  title     = {Practical {Bayesian} optimization of machine learning algorithms},
+  year      = {2012},
+  address   = {Red Hook, NY, USA},
+  month     = dec,
+  pages     = {2951--2959},
+  publisher = {Curran Associates Inc.},
+  series    = {{NIPS}'12},
+  volume    = {2},
+  abstract  = {The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience, rules of thumb, or sometimes brute-force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.},
+  doi       = {10.5555/2999325.2999464},
+  urldate   = {2026-04-21},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}