{"id":"W7131820257","doi":"10.48550/arxiv.2412.07634","title":"Improving the robustness of the Projected Gradient Descent method for nonlinear constrained optimization problems in topology optimization","year":2024,"lang":"en","type":"preprint","venue":"NPARC","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Energy Research and Development; National Research Council Canada","keywords":"Robustness (evolution); Topology optimization; Constrained optimization; Optimization problem; Gradient descent; Nonlinear system; Robust optimization; Nonlinear programming; Derivative-free optimization; Vector optimization","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005751526,0.0003612045,0.0004354481,0.0002827565,0.00007284237,0.00005072869,0.0005043938,0.0004666556,0.00004577628],"category_scores_gemma":[0.0002444543,0.000268094,0.00015824,0.0005228565,0.0001598152,0.00004521161,0.0003914265,0.0008187362,3.290079e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002618719,"about_ca_system_score_gemma":0.0001782708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002565358,"about_ca_topic_score_gemma":0.00003800002,"domain_scores_codex":[0.998134,0.0001342064,0.0007549154,0.000426698,0.00017478,0.0003754454],"domain_scores_gemma":[0.9988278,0.00020452,0.0001980783,0.0005408694,0.0001917235,0.00003698217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007641561,0.00002649268,0.00001809414,0.001186483,0.00008607329,4.719625e-7,0.0005465092,0.9958446,0.0006173147,0.0007171403,0.00008381078,0.0008653469],"study_design_scores_gemma":[0.0003837484,0.00001974339,0.000009587638,0.0002853657,0.0001078375,0.00001181055,0.0001030387,0.997364,0.001048709,0.0003856623,0.0000308495,0.0002495789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001164462,0.0002108385,0.9916704,0.0005696513,0.002908151,0.002713969,0.0001164287,0.0003268835,0.0003192732],"genre_scores_gemma":[0.0698775,0.00009291402,0.9284143,0.00002034212,0.0002007406,0.001037792,0.0001590402,0.0001443354,0.00005301831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06871304,"threshold_uncertainty_score":0.9999771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352451127433859,"score_gpt":0.2458399730500688,"score_spread":0.2323154617757302,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}