Add new figures and update manuscript title, abstract, and keywords

Code/data/width_optimization/2W/ml_FEMdata_B29_H30.csv

@@ -7,4 +7,4 @@ tw1,tw2,Exymax_tw1,Exymax_tw2,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFMmax_frame
 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
-12.66,14.7,0.0449044,0.0617909,0.1170024,88.4725,91.4326,88.0453
+12.6,14.64,0.0450078,0.0615972,0.1161667,88.2463,91.1868,87.1722

Code/data/width_optimization/2W/ml_FEMdata_B34_H30.csv

@@ -7,3 +7,4 @@ tw1,tw2,Exymax_tw1,Exymax_tw2,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFMmax_frame
 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
 8.58,8.88,0.1085222,0.1382075,0.1669545,146.7474,192.9294,94.6607
+15.69,20,0.0512725,0.0460312,0.141023,103.5087,73.2362,99.2099

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
-12.74,12.61,0.0401210,0.0783297,0.1249457,72.9956,112.404,93.3510
-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
+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
 9.78,15.96,0.0710369,0.0433733,0.1167672,143.7781,74.3999,80.5918
-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
+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
 12.34,14.34,0.0461157,0.0635088,0.1160707,90.9982,94.5993,87.6823
+12.68,14.91,0.0449853,0.0597997,0.115753,88.7433,88.8361,86.8080

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

@@ -2,9 +2,10 @@ 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
-21.19,19.38,0.0349424,0.0675929,0.1833217,50.9109,88.8950,118.7431
+21.19,19.38,0.0349424,0.0675929,0.1833217,50.9109,88.895,118.7431
 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
-13.46,20.99,0.0686057,0.0332760,0.1173378,138.9930,60.5905,89.5536
+13.46,20.99,0.0686057,0.033276,0.1173378,138.993,60.5905,89.5536
 8.58,8.88,0.1085222,0.1382075,0.1669545,146.7474,192.9294,94.6607
-15.50,20.00,0.0521125,0.0454009,0.1396136,105.8219,72.7413,98.4961
+15.5,20,0.0521125,0.0454009,0.1396136,105.8219,72.7413,98.4961
+14.8,18.93,0.0537676,0.0493064,0.1387399,109.1427,78.7074,96.7782

Code/data/width_optimization/3W/ml_FEMdata_B29_H45.csv

@@ -15,7 +15,7 @@ tw1,tw2,tw3,Exymax_tw1,Exymax_tw2,Exymax_tw3,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFM
 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
-5.52,8.47,9.33,0.0538202,0.0475062,0.0510451,0.098339,102.5485,83.3491,82.8637,67.9344
+5.96,8.23,9.47,0.0463678,0.0464775,0.0513613,0.1005854,89.1983,85.2212,83.9612,68.0786
 5.81,8.12,9.04,0.0474045,0.0462994,0.0542192,0.100775,91.138,85.5144,88.6423,69.5669
 5.53,7.96,8.84,0.0514931,0.0484667,0.0543194,0.1001148,98.0415,88.6742,90.1816,68.8582
 5.83,7.89,8.95,0.0465888,0.0478258,0.054548,0.1018059,89.3979,88.5887,90.4094,69.1392

Code/data/width_optimization/3W/ml_FEMdata_B34_H45.csv

@@ -15,6 +15,6 @@ tw1,tw2,tw3,Exymax_tw1,Exymax_tw2,Exymax_tw3,Eyymax_tf,TFMmax_tw1,TFMmax_tw2,TFM
 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
-7.39,9.31,10.15,0.0379502,0.0469769,0.053119,0.0937488,79.7768,86.3154,87.0519,69.8441
+7.34,9.28,10.13,0.0383401,0.0470196,0.0529832,0.0937131,80.7634,86.7811,87.0794,69.6596
 6.88,9.05,9.83,0.0425174,0.0431259,0.0541058,0.0920949,89.8694,89.8644,88.4408,68.6215
 6.83,9.03,9.71,0.0422012,0.0464421,0.0538225,0.092772,90.4471,89.198,89.9087,68.9794

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
-8.93,10.69,11.92,0.0246997,0.0417768,0.0464886,0.1013018,42.8515,55.8740,68.8582,72.3628
-14.65,7.11,11.38,0.0027074,0.0739921,0.0456774,0.1320175,3.2259,113.0944,74.3599,81.7340
+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
 6.29,6.52,9.56,0.0400088,0.0663047,0.0487848,0.1169582,74.9831,119.2527,87.0856,69.9576
-4.58,8.02,12.59,0.0837647,0.0655233,0.0305480,0.0898686,157.3271,109.3948,50.2318,58.5561
-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
+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
 10.54,4.85,8.21,0.0178929,0.1071379,0.0903543,0.1374885,13.2657,171.3972,112.4605,79.8828
-7.07,15.26,4.16,0.0199412,0.0309740,0.1744638,0.2911736,31.3565,37.3897,285.5494,166.6332
-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
+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
 5.94,8.38,9.28,0.0463239,0.0448421,0.0532387,0.1007058,89.2364,82.0068,85.7161,69.8185
-5.68,7.97,9.00,0.0493762,0.0478416,0.0536288,0.1003415,94.4223,88.5502,88.8948,68.5487
+5.69,7.97,9.02,0.0492756,0.0478664,0.0534413,0.1004192,94.16882,88.57847,88.69432,68.470374

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
-8.93,10.69,11.92,0.0314285,0.0361880,0.0500572,0.0968860,60.3168,73.0743,75.1730,70.8353
+8.93,10.69,11.92,0.0314285,0.036188,0.0500572,0.096886,60.3168,73.0743,75.173,70.8353
 14.65,7.11,11.38,0.0041545,0.0770628,0.0446173,0.1298872,5.7242,140.3068,76.6369,85.1589
-6.29,6.52,9.56,0.0425330,0.0678026,0.0470426,0.1058771,91.4702,145.2878,86.7668,68.0555
-4.58,8.02,12.59,0.1047867,0.0870891,0.0314758,0.0834590,202.6256,124.2751,48.3992,57.2506
+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
 10.95,6.12,10.09,0.0169834,0.0839863,0.0570973,0.1329752,18.5951,160.5635,88.2842,83.5555
-13.03,8.96,14.90,0.0109994,0.0652393,0.0298515,0.1160830,14.5435,117.7404,55.5012,82.8776
-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
+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
 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
-7.82,9.84,14.42,0.0421770,0.0474820,0.0297936,0.0934205,88.3429,98.3484,53.4841,67.0666
-5.72,11.78,13.37,0.0772789,0.0353789,0.0293972,0.0719482,166.3376,67.8034,55.0039,54.5020
+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
 10.54,4.85,8.21,0.0161377,0.1107135,0.0903302,0.1383831,18.5385,195.3177,110.0121,81.7428
-7.07,15.26,4.16,0.0205172,0.0282205,0.1826803,0.2850700,40.8943,38.3046,285.0250,149.7966
-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
+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
+7.21,9.27,9.82,0.0389181,0.0460074,0.0552465,0.0930625,82.203,85.0636,90.3346,70.8627
+6.81,9.02,9.65,0.042284433,0.046292119,0.054206595,0.092638396,90.54817,89.06076492,90.38183775,69.0909405

Code/data/width_optimization/5W/ml_FEMdata_B34_H60.csv

@@ -63,6 +63,6 @@ tw1,tw2,tw3,tw4,tw5,Exymax_tw1,Exymax_tw2,Exymax_tw3,Exymax_tw4,Exymax_tw5,Eyyma
 10.08,13.19,12.66,9.43,12.79,0.0182058,0.0173443,0.0443796,0.0671675,0.0281014,0.1455139,27.9228,26.9374,63.7859,95.6849,54.3139,93.6209
 8.96,4.52,5.58,6.91,9.82,0.0182504,0.0894588,0.1212475,0.0631662,0.024132,0.1199809,21.3667,157.3141,204.0845,99.8104,32.8082,61.9136
 5.68,13.91,12.58,11.42,5.93,0.066269,0.0164049,0.0266002,0.0471815,0.0612316,0.1382278,123.0662,24.2749,43.1425,56.6075,108.8461,93.968
-5.74,7.35,8.73,6.54,5,0.0616588,0.0548414,0.0568781,0.0715819,0.0582073,0.1218811,93.0957,93.5817,82.9841,81.4939,72.9981,78.5423
+5.97,7.38,8.56,6.7,5,0.0563488,0.0518778,0.0596584,0.0712373,0.058239,0.1229254,84.9512,90.6505,87.9108,81.0599,74.1754,79.1536
 5.7,7.42,8.45,6.09,5,0.0602438,0.0528245,0.0588412,0.0775535,0.0574888,0.1230745,92.0594,89.7108,87.3874,91.3958,72.3656,77.627
 5.76,7.39,8.42,6.17,5,0.0581466,0.0520777,0.0593142,0.077353,0.0579703,0.1234245,90.1357,89.9638,88.5223,90.3566,72.4717,77.916

Code/models/width_optimization/2W/ml_models/per_output_models_B29_H30/it1/cv_detailed_per_output_B29_H30.csv

+output,model,cv_rmse,std_rmse,cv_rmse_dispersion,cv_mae,cv_r2,BEST_PARAMS,fit_time_sec,gpr_kernel
+exymax_tw1,SVR,0.0024756253409925536,0.003162723363436417,1.2775452371837432,0.0024756253409925536,,"{""svr__C"": 1776.5766649807683, ""svr__epsilon"": 0.00032780432870046914, ""svr__gamma"": 0.006225026900894044}",16.06,
+exymax_tw1,GaussianProcess,0.003544749506220166,0.00339206480908583,0.9569265199511527,0.003544749506220166,,"{""gpr__amplitude"": 74.13954798652198, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 1.845551297039509, ""gpr__n_restarts_optimizer"": 1, ""gpr__noise"": 4.912618463633794e-12, ""gpr__rq_alpha"": 0.05514509165905166}",29.56,"2.95**2 * Matern(length_scale=4.58, nu=2.5) + WhiteKernel(noise_level=4.91e-12)"
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Code/models/width_optimization/2W/ml_models/per_output_models_B29_H30/it1/cv_summary_per_output_B29_H30.csv

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Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/cv_detailed_per_output_B34_H30.csv

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Code/models/width_optimization/2W/ml_models/per_output_models_B34_H30/it1/cv_summary_per_output_B34_H30.csv

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Code/models/width_optimization/2W/rbf_models/per_output_models_B29_H30/it2/rbf_LOO_results.csv

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Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it1/cv_summary_per_output_B29_H45.csv

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Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/cv_detailed_per_output_B34_H45.csv

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Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it1/cv_summary_per_output_B34_H45.csv

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Code/models/width_optimization/3W/rbf_models/per_output_models_B29_H45/it2/rbf_LOO_results.csv

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Code/models/width_optimization/3W/rbf_models/per_output_models_B34_H45/it2/rbf_LOO_results.csv

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Code/models/width_optimization/5W/ml_models/per_output_models_B34_H60/it1/cv_detailed_per_output_B34_H60.csv

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Code/models/width_optimization/5W/ml_models/per_output_models_B34_H60/it1/cv_summary_per_output_B34_H60.csv

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

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

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+TFM_tw1,110.55081870186555
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Code/reports/width_optimization/2W/rbf_optimization/it2/optimization_results_2W_B29_H30_TFD_W90.csv

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Code/reports/width_optimization/2W/rbf_optimization/it2/optimization_results_2W_B34_H30_TFD_W90.csv

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

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

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Code/reports/width_optimization/3W/rbf_optimization/it2/optimization_results_3W_B29_H45_TFD_W90.csv

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Code/reports/width_optimization/3W/rbf_optimization/it2/optimization_results_3W_B34_H45_TFD_W90.csv

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+tw3_optimal,9.673693648288205
+Objective_score,-1.5036470193089083e-06
+Exy_tw1,0.04207613237690064
+Exy_tw2,0.04671777307468361
+Exy_tw3,0.05387304603413823
+TFM_tw1,90.00000004002021
+TFM_tw2,90.00000003705242
+TFM_tw3,90.00000005796741
+TFM_frame,69.04446249717469

Code/reports/width_optimization/5W/ml_optimization/it1/optimization_results_5W_B34_H60_TFD_W90.csv

+Parameter,Value
+Configuration_W,5.0
+Configuration_B,34.0
+Configuration_H,60.0
+Configuration_TFD_W,90.0
+Iteration,1.0
+tw1_optimal,5.759921476551572
+tw2_optimal,7.3711507027129075
+tw3_optimal,8.456689063290359
+tw4_optimal,6.668376537250182
+tw5_optimal,5.0
+Objective_score,16.960815946902244
+Exy_tw1,0.06149628053180933
+Exy_tw2,0.053127626566851494
+Exy_tw3,0.06014397740364075
+Exy_tw4,0.07067358204821853
+Exy_tw5,0.05734081700027541
+TFM_tw1,90.00000302054411
+TFM_tw2,90.00000046377134
+TFM_tw3,90.00000018993417
+TFM_tw4,90.00001156625868
+TFM_tw5,73.03918138711484
+TFM_frame,74.84972818192045

Code/src/width_optimization/run_ml.sh

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

Code/src/width_optimization/run_rbf.sh

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

@@ -25,7 +25,7 @@
 
 \begin{document}
 
-\title{Adaptive FEM-validated surrogate optimization of buckling-delayed shear-link dampers for seismic damage mitigation}
+\title{Damage-aware surrogate optimization of buckling-delayed shear-link dampers with adaptive finite element validation}
 
 \author[1]{J. Irazábal}
 \author[1,2]{J. Ramírez}
@@ -36,7 +36,7 @@
 \author[4]{L. Bozzo}
 
 \authormark{IRAZÁBAL \textsc{et al.}}
-\titlemark{ADAPTIVE FEM-VALIDATED SURROGATE OPTIMIZATION OF BDSL DAMPERS}
+\titlemark{DAMAGE-AWARE SURROGATE OPTIMIZATION OF SHEAR-LINK DAMPERS}
 
 \address[1]{\orgname{Centre Internacional de Metodes Numerics en Enginyeria (CIMNE)}, \orgaddress{\city{Barcelona}, \country{Spain}}}
 \address[2]{\orgname{Universitat Politecnica de Catalunya (UPC)}, \orgaddress{\city{Barcelona}, \country{Spain}}}
@@ -45,9 +45,9 @@
 
 \corres{J. Irazábal. \email{jirazabal@cimne.upc.edu}}
 
-\abstract[Abstract]{Buckling-delayed shear-link (BDSL) dampers are used in seismic-resistant structures as passive devices that concentrate energy dissipation while limiting damage to the primary system. Their geometric optimization requires a compromise between high energy dissipation and control of local damage. Finite element method (FEM) models can reproduce with high accuracy the nonlinear cyclic response of these devices and provide damage indicators and local distortion, but their computational cost prevents their direct use inside iterative optimization loops. This work proposes an adaptive surrogate-assisted optimization framework for BDSL dampers. First, experimentally calibrated nonlinear FEM models are used to generate ground-truth datasets for dampers with different geometric and mechanical configurations. Supervised learning models are first evaluated, where Support Vector Regression (SVR) and Gaussian Process Regression (GPR)—both based on radial kernel functions—consistently provide the highest predictive accuracy. Motivated by this observation, Radial Basis Function (RBF) surrogates are subsequently introduced as a computationally efficient alternative. The surrogate predictions are coupled with a Differential Evolution algorithm through a damage-aware objective function that limits the damage and uses dissipated energy as a tie-breaking performance criterion. In addition, SHapley Additive exPlanations (SHAP) are employed to quantify the influence of window thickness on damage distribution, with particular emphasis on the response of the surrounding frame. Optimized geometries are finally re-evaluated with FEM. When the surrogate error exceeds the adopted tolerances, the new FEM result is added to the dataset and the surrogate models are retrained. The proposed framework provides a scalable route for an efficient damage-aware optimization of seismic energy dissipation devices.}
+\abstract[Abstract]{Buckling-delayed shear-link (BDSL) dampers are used in seismic-resistant structures as passive devices that concentrate energy dissipation while limiting damage to the primary system. Their geometric optimization requires a compromise between high energy dissipation and control of local damage. Finite element method (FEM) models can reproduce with high accuracy the nonlinear cyclic response of these devices and provide damage indicators and local distortion, but their computational cost prevents their direct use inside iterative optimization loops. This work proposes an adaptive surrogate-assisted optimization framework for BDSL dampers. First, experimentally calibrated nonlinear FEM models are used to generate ground-truth datasets for dampers with different geometric and mechanical configurations. Supervised learning models are first evaluated, where Support Vector Regression (SVR) and Gaussian Process Regression (GPR)—both based on radial kernel functions—consistently provide high predictive accuracy. Motivated by this observation, Radial Basis Function (RBF) surrogates are introduced as a computationally efficient alternative. The surrogate predictions are coupled with a Differential Evolution algorithm through a damage-aware objective function that limits the damage and uses dissipated energy as a tie-breaking performance criterion. Optimized geometries are finally re-evaluated with FEM. When the surrogate error exceeds the adopted tolerances, the new FEM result is added to the dataset and the surrogate models are retrained. In addition, SHapley Additive exPlanations (SHAP) are employed to quantify the influence of window thickness on damage distribution, with particular emphasis on the response of the surrounding frame. The proposed framework provides a scalable route for an efficient damage-aware optimization of seismic energy dissipation devices.}
 
-\keywords{Buckling-delayed shear link, seismic energy dissipation, surrogate modelling, machine learning, radial basis functions, Differential Evolution, FEM validation, TFDMap}
+\keywords{Buckling-delayed shear link, seismic energy dissipation, FEM validation, surrogate modelling, machine learning, radial basis functions, Differential Evolution, SHAP.}
 
 \jnlcitation{\cname{%
 \author{Irazábal J.},
@@ -57,7 +57,7 @@
 \author{Rastellini F.},
 \author{Bozzo G.}, and
 \author{Bozzo L.}}.
-\ctitle{Adaptive FEM-validated surrogate optimization of buckling-delayed shear-link dampers for seismic damage mitigation.} \cjournal{\it Journal.} \cvol{2026;00(00):1--18}.}
+\ctitle{Damage-aware surrogate optimization of buckling-delayed shear-link dampers with adaptive finite element validation.} \cjournal{\it Journal.} \cvol{2026;00(00):1--18}.}
 
 \maketitle
 
@@ -83,7 +83,7 @@ All these works demonstrate the increasing interest in applying FEM-based and da
 
 The present work addresses this gap through a damage-aware surrogate-assisted optimization methodology in which the objective is not only to maximize distortion or energy dissipation, but also to balance dissipative performance with damage indicators derived from FEM simulations. The proposed approach combines: (i) experimentally calibrated nonlinear FEM models used as numerical ground truth; (ii) supervised surrogate models trained to predict local damage and distortion indicators; (iii) a Differential Evolution (DE) optimizer; and (iv) an adaptive FEM validation and retraining loop.
 
-Figure \ref{fig:MethodologyFlowChart} summarizes the proposed workflow. The different stages of the methodology, together with the surrogate modelling, optimization strategy, validation procedure, and corresponding results, are described in the following sections.
+Figure \ref{fig:MethodologyFlowChart} summarizes the proposed workflow. The different stages of the methodology, together with the surrogate modelling, optimization strategy, validation procedure and corresponding results, are described in the following sections.
 
 \begin{figure}[!ht]
   \centering
@@ -94,12 +94,12 @@ Figure \ref{fig:MethodologyFlowChart} summarizes the proposed workflow. The diff
 
 \section{Buckling-delayed shear-link damper}\label{sec:device}
 
-The BDSL dampers analysed in this work, with one representative configuration shown in Figure \ref{fig:Device}, are designed to concentrate energy dissipation in localized reduced-thickness zones, hereafter referred to as \ti{windows}, while preserving the structural integrity of the surrounding frame. The dissipative element is connected to a load-transfer system through a mechanism that allows imposed in-plane displacement while preventing axial force transmission, thereby promoting a shear-dominated response. Under cyclic loading, plastic deformation is intended to concentrate in the windows, whereas the frame provides load transfer and stability.
+The BDSL dampers analysed in this work, with one representative configuration shown in Figure \ref{fig:Device}, are designed to concentrate energy dissipation in localized reduced-thickness zones, hereafter referred to as windows, while preserving the structural integrity of the surrounding frame. The optimization variables correspond to the window thicknesses, whereas the frame dimensions are kept fixed. The dissipative element is connected to a load-transfer system through a mechanism that allows imposed in-plane displacement while preventing axial force transmission, thereby promoting a shear-dominated response. Under cyclic loading, plastic deformation is intended to concentrate in the windows, whereas the frame provides load transfer and stability.
 
 \begin{figure}[!ht]
   \centering
-  \includegraphics[width=0.20\textwidth]{./Figures/Device.png}
-  \caption{Representative BDSL damper configuration analysed in this work. The optimization variables correspond to the window thicknesses, while the surrounding frame dimensions remain fixed. \red{Replace figure with a version including geometric dimensions. Reduce caption.}}
+  \includegraphics[width=0.25\textwidth]{./Figures/Device.png}
+  \caption{Representative BDSL damper configuration considered in the optimization.}
   \label{fig:Device}
 \end{figure}
 
@@ -110,20 +110,24 @@ The design variables considered in this work are the window thicknesses
 \mathbf{x}=\left[t_{w,1},t_{w,2},\ldots,t_{w,W}\right],
 \label{eq:design_vector}
 \end{equation}
-where $W$ denotes the number of windows. The width and height identifiers of the device are represented by $B$ and $H$, respectively. Five geometry families are considered, as shown in Figure \ref{fig:GeometryFamilies}: H30\_B29, H30\_B34, H45\_B29, H45\_B34, and H60\_B34. Devices with $H=30$ cm have two windows, those with $H=45$ cm have three windows, and those with $H=60$ cm have five windows.
+where $W$ denotes the number of windows. The width and height identifiers of the device are represented by $B$ and $H$, respectively. Five geometry families are considered, as shown in Figure \ref{fig:GeometryFamilies}: H30\_B29, H30\_B34, H45\_B29, H45\_B34 and H60\_B34. Devices with $H=30$ cm have two windows, those with $H=45$ cm have three windows, and those with $H=60$ cm have five windows.
 
 \begin{figure}[ht!]
-\centering
-\fbox{\parbox[c][0.25\textheight][c]{0.85\textwidth}{\centering Placeholder for geometry family representations.}}
-\caption{Geometry families considered in the current implementation.}
-\label{fig:GeometryFamilies}
+  \centering
+  \includegraphics[width=0.15\textwidth]{Figures/H30_B29.png}\label{fig:H30_B29}
+  \includegraphics[width=0.15\textwidth]{Figures/H30_B34.png}\label{fig:H30_B34}
+  \includegraphics[width=0.15\textwidth]{Figures/H45_B29.png}\label{fig:H45_B29}
+  \includegraphics[width=0.15\textwidth]{Figures/H45_B34.png}\label{fig:H45_B34}
+  \includegraphics[width=0.15\textwidth]{Figures/H60_B34.png}\label{fig:H60_B34}
+  \caption{BDSL families considered for optimization in the current study. From left to right: H30\_B29, H30\_B34, H45\_B29, H45\_B34 and H60\_B34.}
+  \label{fig:GeometryFamilies}
 \end{figure}
 
 The admissible thickness ranges are defined according to the geometry family and manufacturing constraints. The main characteristics of each family are summarized in Table~\ref{tab:families}. In all cases, the frame thickness is kept constant at 30 mm.
 
 \begin{table}[ht!]
 \centering
-\caption{Geometry families considered in the current implementation and admissible window thickness ranges.}
+\caption{Geometry families considered for optimization in the current study and admissible window thickness ranges.}
 \label{tab:families}
 \begin{tabular}{lllllll}
 \toprule
@@ -162,19 +166,12 @@ The model was calibrated and validated against cyclic experimental tests perform
   \label{fig:CalibrationCurves}
 \end{figure}
 
-Once validated, the FEM model is used to generate the datasets required for surrogate training and optimization. This is essential because the proposed optimization strategy relies on internal response quantities that cannot be directly measured experimentally with sufficient spatial resolution. These quantities include local damage indicators in the dissipative windows and in the surrounding frame, as well as local distortion measures associated with the activation of the dissipative mechanism.
+Once validated, the FEM model is used to generate the datasets required for surrogate training and optimization. The optimization strategy relies on local damage indicators in the dissipative windows and surrounding frame, together with local distortion measures associated with the activation of the dissipative mechanism. Since these quantities are difficult to measure experimentally with sufficient spatial resolution, the use of a high-fidelity FEM model provides access to the internal state variables and local fields governing damage evolution and energy dissipation.
 
-The main damage indicator adopted in this work is the Triaxial Failure Damage Map (TFDMap) \cite{Rastellini2016}. This stress-triaxiality-based indicator evaluates the proximity of each material point to ductile failure by comparing its stress triaxiality and accumulated equivalent plastic strain with a reference failure envelope \cite{Rice1969,Bao2004,Wierzbicki2005,Bai2008}. In this study, the TFDMap is used as a post-processing damage-screening indicator, not as a constitutive fracture criterion. Its purpose is therefore not to explicitly predict crack initiation, but to compare geometrical configurations and ensure that optimized designs remain within acceptable damage levels.
+The damage indicator adopted in this work is the Triaxial Failure Damage Map (TFDMap) \cite{Rastellini2016}. This stress-triaxiality-based indicator evaluates the proximity of each material point to ductile failure by comparing its stress triaxiality and accumulated equivalent plastic strain with a reference failure envelope \cite{Rice1969,Bao2004,Wierzbicki2005,Bai2008}. In this study, the TFDMap is used as a post-processing damage-screening indicator, not as a constitutive fracture criterion. Its purpose is therefore not to explicitly predict crack initiation, but to compare geometrical configurations and ensure that optimized designs remain within acceptable damage levels.
 
 For optimization purposes, damage is evaluated separately in the windows and in the frame. The window damage indicator $\TFD_i$ is defined as the average TFDMap value of the 12 nodes with the highest values in window $i$, while the corresponding frame indicator is denoted by $\TFD_f$. This aggregation captures the most critical damage levels while reducing sensitivity to isolated numerical peaks. In addition, the maximum local shear distortion in each window is denoted by $\varepsilon_{xy,i}$ and is used as a proxy for dissipative activation. The contribution of each window is weighted according to its effective geometric volume, so that the optimization accounts not only for point-wise strain values but also for the material volume involved in the dissipation process.
 
-\begin{figure}[ht!]
-\centering
-\fbox{\parbox[c][0.25\textheight][c]{0.85\textwidth}{\centering Placeholder for an example of the TFDMap and distortion distribution in a device.}}
-\caption{Example of TFDMap and local distortion fields used to define the damage and deformation indicators.}
-\label{fig:TFDMapDistortion}
-\end{figure}
-
 \section{Dataset generation and surrogate modelling}\label{sec:surrogates}
 
 \subsection{Design of experiments}\label{subsec:doe}
@@ -184,20 +181,20 @@ The FEM campaign is designed to cover the admissible design domain of each devic
 To improve the robustness of the surrogate models near the admissible limits, the sampling domain is extended slightly beyond the actual optimization ranges (see Table~\ref{tab:families}). This strategy reduces the risk of extrapolation when evaluating candidate solutions close to the true design limits. The ranges employed during the DoE are summarized in Table~\ref{tab:families_doe}.
 
 \begin{table}[ht!]
-\centering
-\caption{Geometry families and window thickness ranges considered during the DoE generation.}
-\label{tab:families_doe}
-\begin{tabular}{lllllll}
-\toprule
-Family & Height $H$ & Width $B$ & Windows & Frame thickness & Design variables & Thickness bounds (DoE) \\
-\midrule
-H30\_B29 & 30 cm & 29 cm & 2 & 30 mm & $t_{w,1},t_{w,2}$ & 8--22 mm \\
-H30\_B34 & 30 cm & 34 cm & 2 & 30 mm & $t_{w,1},t_{w,2}$ & 8--22 mm \\
-H45\_B29 & 45 cm & 29 cm & 3 & 30 mm & $t_{w,1},t_{w,2},t_{w,3}$ & 4--16 mm \\
-H45\_B34 & 45 cm & 34 cm & 3 & 30 mm & $t_{w,1},t_{w,2},t_{w,3}$ & 4--16 mm \\
-H60\_B34 & 60 cm & 34 cm & 5 & 30 mm & $t_{w,1},\ldots,t_{w,5}$ & 4--14 mm \\
-\bottomrule
-\end{tabular}
+  \centering
+  \caption{Geometry families and window thickness ranges considered during the DoE generation.}
+  \label{tab:families_doe}
+  \begin{tabular}{lllllll}
+    \toprule
+    Family & Height $H$ & Width $B$ & Windows & Frame thickness & Design variables & Thickness bounds (DoE) \\
+    \midrule
+    H30\_B29 & 30 cm & 29 cm & 2 & 30 mm & $t_{w,1},t_{w,2}$ & 8--22 mm \\
+    H30\_B34 & 30 cm & 34 cm & 2 & 30 mm & $t_{w,1},t_{w,2}$ & 8--22 mm \\
+    H45\_B29 & 45 cm & 29 cm & 3 & 30 mm & $t_{w,1},t_{w,2},t_{w,3}$ & 4--16 mm \\
+    H45\_B34 & 45 cm & 34 cm & 3 & 30 mm & $t_{w,1},t_{w,2},t_{w,3}$ & 4--16 mm \\
+    H60\_B34 & 60 cm & 34 cm & 5 & 30 mm & $t_{w,1},\ldots,t_{w,5}$ & 4--14 mm \\
+    \bottomrule
+  \end{tabular}
 \end{table}
 
 For every sampled configuration, a FEM simulation is performed, and the resulting dataset stores both the input variables and the corresponding structural response quantities. The target outputs include the maximum local distortions in each window, denoted by $\varepsilon_{xy,i}$, together with the window damage indicators $\TFD_i$ and the frame damage indicator $\TFD_f$.
@@ -210,11 +207,11 @@ The number of samples in all cases is defined as a power of two. This choice fac
 
 \subsection{Supervised ML surrogate models}\label{subsec:ml_models}
 
-This work compares several supervised surrogate models for predicting FEM-derived damage and distortion indicators. The considered models cover three families: tree-based methods, including Random Forest (RF) \cite{Breiman2001}, Gradient Boosting Regression (GBR) \cite{Friedman2001}, and XGBoost \cite{Chen2016}; kernel-based methods, including Support Vector Regression (SVR) \cite{Drucker1996} and Gaussian Process Regression (GPR) \cite{Williams1995}; and neural-network models, represented by the Multilayer Perceptron (MLP) \cite{Rosenblatt1958,Rumelhart1986}. RF relies on bootstrap aggregation of decision trees, GBR and XGBoost are sequential boosting approaches, SVR and GPR exploit kernel functions to model nonlinear relationships, and MLP approximates nonlinear input--output mappings through interconnected layers.
+This work compares several supervised surrogate models for predicting FEM-derived damage and distortion indicators. The considered models cover three families: tree-based methods, including Random Forest (RF) \cite{Breiman2001}, Gradient Boosting Regression (GBR) \cite{Friedman2001} and XGBoost \cite{Chen2016}; kernel-based methods, including Support Vector Regression (SVR) \cite{Drucker1996} and Gaussian Process Regression (GPR) \cite{Williams1995}; and neural-network models, represented by the Multilayer Perceptron (MLP) \cite{Rosenblatt1958,Rumelhart1986}. RF relies on bootstrap aggregation of decision trees, GBR and XGBoost are sequential boosting approaches, SVR and GPR exploit kernel functions to model nonlinear relationships and MLP approximates nonlinear input--output mappings through interconnected layers.
 
-For each geometry family, an independent regression model is trained for every target output. The input vector contains the window thicknesses of the corresponding device, while the outputs are the local distortion indicators $\varepsilon_{xy,i}$, the window damage indicators $\TFD_i$, and the frame damage indicator $\TFD_f$. Therefore, for a device with $W$ windows, $2W+1$ surrogate models are trained: $W$ for distortion, $W$ for window damage, and one for frame damage. For each output, all candidate algorithms are evaluated and the selected model is stored.
+For each geometry family, an independent regression model is trained for every target output. The input vector contains the window thicknesses of the corresponding device, while the outputs are the local distortion indicators $\varepsilon_{xy,i}$, the window damage indicators $\TFD_i$ and the frame damage indicator $\TFD_f$. Therefore, for a device with $W$ windows, $2W+1$ surrogate models are trained: $W$ for distortion, $W$ for window damage and one for frame damage. For each output, all candidate algorithms are evaluated and the selected model is stored.
 
-Hyperparameters are optimized using Bayesian optimization \cite{Snoek2012}, with 40 evaluations per model. The first 10 evaluations are randomly sampled to explore the search space, while the remaining 30 are guided by the Bayesian surrogate model. This strategy provides a more efficient alternative to exhaustive grid search, particularly given the number of geometry families, target outputs, and candidate algorithms considered.
+Hyperparameters are optimized using Bayesian optimization \cite{Snoek2012}, with 40 evaluations per model. The first 10 evaluations are randomly sampled to explore the search space, while the remaining 30 are guided by the Bayesian surrogate model. This strategy provides a more efficient alternative to exhaustive grid search, particularly given the number of geometry families, target outputs and candidate algorithms considered.
 
 The cross-validation strategy is adapted to the dataset size. Leave-One-Out validation is used for $N\leq20$, repeated five-fold cross-validation with five repetitions for $21\leq N\leq80$, and shuffled five-fold cross-validation for larger datasets. For small datasets, the search spaces of tree-based models are restricted to reduce overfitting.