{"id":"W3020024945","doi":"10.1103/physrevresearch.2.033110","title":"Neural network solutions to differential equations in nonconvex domains: Solving the electric field in the slit-well microfluidic device","year":2020,"lang":"en","type":"article","venue":"Physical Review Research","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Estimator; Measure (data warehouse); Electric field; Stochastic neural network; Differential equation; Field (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005558833,0.0004218231,0.0004739905,0.0002408001,0.0002808344,0.0005437891,0.0005706,0.001263194,0.001557482],"category_scores_gemma":[0.001639551,0.0002271601,0.0003701679,0.000290004,0.0006458071,0.000647609,0.0004896944,0.0008931256,0.0001255048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007468712,"about_ca_system_score_gemma":0.0009991656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00744016,"about_ca_topic_score_gemma":0.006844018,"domain_scores_codex":[0.9999177,0.00002589467,0.000003956014,0.00001587228,0.00002231228,0.00001427616],"domain_scores_gemma":[0.9994403,0.0004248675,0.00003932833,0.00001961,0.00005420606,0.00002169888],"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":[0.00002433446,0.00001832564,0.000396152,0.00003901756,0.000009564226,0.0000454478,0.00001955929,0.9874264,0.001098847,0.005938974,0.0003312272,0.004652075],"study_design_scores_gemma":[0.000002410447,0.000003437849,0.00003483334,0.000002034474,6.238175e-7,0.000002964979,0.000003851809,0.998762,0.0002195187,0.0008784939,0.00008875973,0.000001105435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3044128,0.0008830272,0.670869,0.00211178,0.0001895816,0.0001038268,0.0003251415,0.0003564893,0.02074836],"genre_scores_gemma":[0.8676571,0.0004834978,0.1218941,0.0002405095,0.00005032016,0.0001491531,0.0002316896,0.00007512182,0.009218469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00744016,"threshold_uncertainty_score":0.01479369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1162392690091797,"score_gpt":0.3952680534104979,"score_spread":0.2790287844013183,"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."}}