{"id":"W4416939867","doi":"10.2139/ssrn.5851456","title":"HeatGen: A Guided Diffusion Framework for Multiphysics Heat Sink Design Optimization","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Multiphysics; Surrogate model; Heat sink; Probabilistic logic; Pressure drop; Topology optimization; Optimization problem; Boundary value problem; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.004390424,0.001733012,0.001701875,0.0009875557,0.002422811,0.001226732,0.003655524,0.001462552,0.00005271238],"category_scores_gemma":[0.00187002,0.001854824,0.001184733,0.001964492,0.000254742,0.001446973,0.001754023,0.009632829,0.0000209704],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.009522416,"about_ca_system_score_gemma":0.01956485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005270266,"about_ca_topic_score_gemma":0.00002415823,"domain_scores_codex":[0.9854983,0.001221979,0.002398856,0.002687918,0.001351322,0.006841633],"domain_scores_gemma":[0.9909019,0.001655397,0.001663693,0.001977404,0.003270912,0.0005306748],"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":[0.0003035695,0.0005665962,0.00001195161,0.00008177256,0.0005622814,0.000005312144,0.0008394716,0.8266829,0.00007862258,0.0970016,0.00005577688,0.07381014],"study_design_scores_gemma":[0.002422777,0.0006434682,0.000003793851,0.0006864372,0.0002091919,0.0002008635,0.0003210774,0.6518993,0.0003885273,0.3419312,0.0002301479,0.001063253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000262052,0.004228298,0.9834585,0.002191971,0.005003064,0.004597772,0.00005728653,0.0003052226,0.0001316712],"genre_scores_gemma":[0.00301986,0.03164025,0.9598402,0.0006113967,0.001602198,0.0004016488,0.00009034453,0.000181741,0.002612361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2449296,"threshold_uncertainty_score":0.9998338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02780535587752588,"score_gpt":0.3117422621490365,"score_spread":0.2839369062715106,"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."}}