{"id":"W4391782195","doi":"10.1088/2632-2153/ad2493","title":"Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion","year":2024,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Laser-Plasma Interactions and Diagnostics","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Air Force Office of Scientific Research; National Nuclear Security Administration; University of Rochester; U.S. Department of Energy","keywords":"Hessian matrix; Computer science; Algorithm; Artificial intelligence; Leverage (statistics); Estimation theory; Differentiable function; Machine learning; Applied mathematics; Mathematics; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001544857,0.0004650528,0.0003147079,0.0007618106,0.0002600823,0.001040249,0.0006122359,0.0004019843,0.002164953],"category_scores_gemma":[0.004076239,0.0001987557,0.000365218,0.0004724877,0.001271274,0.0009863216,0.001125767,0.0008124642,0.0002964197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006176297,"about_ca_system_score_gemma":0.0006521838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001759072,"about_ca_topic_score_gemma":0.00107339,"domain_scores_codex":[0.9995486,0.0001385634,0.00002272524,0.00005038451,0.0002133214,0.0000264191],"domain_scores_gemma":[0.998276,0.0009258196,0.0001736623,0.0003274082,0.0002579715,0.00003918363],"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":[0.0001519341,0.0001075968,0.002572487,0.0002007595,0.00002728187,0.0002809274,0.0002824908,0.5054638,0.08222085,0.304838,0.002595848,0.101258],"study_design_scores_gemma":[0.000004518633,0.00001644676,0.0001951411,0.000006658951,0.00000206475,0.00003246699,0.00001212551,0.9690213,0.01171378,0.01756688,0.001420742,0.000007931674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02716053,0.00006557623,0.9677059,0.0002152501,0.00003033665,0.00002928714,0.0000541728,0.000761195,0.003977652],"genre_scores_gemma":[0.6018615,0.000131165,0.395247,0.00009772604,0.0000265024,0.00006244335,0.0001243149,0.0003144789,0.002134899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002164953,"threshold_uncertainty_score":0.008170128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172549593839667,"score_gpt":0.350563045502461,"score_spread":0.3333080861184943,"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."}}