{"id":"W3141613148","doi":"10.1007/978-3-030-95025-5_41","title":"A Nonlinear Optimized Schwarz Preconditioner for Elliptic Optimal Control Problems","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computational science and engineering","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Schwarz alternating method; Preconditioner; Additive Schwarz method; Nonlinear system; Section (typography); Mathematics; Applied mathematics; Control (management); Computer science; Mathematical optimization; Control theory (sociology); Engineering; Domain decomposition methods; Structural engineering; Iterative method; Physics; Artificial intelligence; Finite element method","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"],"consensus_categories":[],"category_scores_codex":[0.0004815161,0.000344862,0.000421987,0.0004159947,0.0001545874,0.00007375733,0.0002507496,0.0001261547,0.0000736448],"category_scores_gemma":[0.000463119,0.0003674006,0.00007537124,0.0002315313,0.0001470173,0.0002142069,0.00006459248,0.0004968732,0.000003226567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003350915,"about_ca_system_score_gemma":0.0001037352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.348615e-7,"about_ca_topic_score_gemma":2.759505e-7,"domain_scores_codex":[0.9982473,0.000007003696,0.0004340856,0.00041905,0.0005465667,0.0003459615],"domain_scores_gemma":[0.9975008,0.002012684,0.00008300779,0.0001288534,0.0001694769,0.0001051121],"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":[0.000007574185,0.000008141086,5.692762e-7,0.0002110371,0.00002899599,0.000002686864,0.00008023266,0.9817251,0.00009748346,0.01279958,0.000006703147,0.005031884],"study_design_scores_gemma":[0.0004844986,0.00005731912,0.000005752984,0.0001158737,0.00002143398,0.00002512954,9.232878e-7,0.914084,0.00003261635,0.08033382,0.004464271,0.0003743622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001288785,0.0006012147,0.9969361,0.0001525552,0.0003973101,0.0006790175,0.0000986571,0.0001954648,0.000810769],"genre_scores_gemma":[0.02176257,0.0000445087,0.9774503,0.0001218503,0.0001932508,0.0001883704,0.00006990185,0.0001020314,0.00006724609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06764112,"threshold_uncertainty_score":0.9998778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621946726298982,"score_gpt":0.2570629610942613,"score_spread":0.2408434938312715,"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."}}