{"id":"W4392005228","doi":"10.1016/j.cpc.2024.109129","title":"Quasi-optimal domain decomposition method for neural network-based computation of the time-dependent Schrödinger equation","year":2024,"lang":"en","type":"article","venue":"Computer Physics Communications","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Carleton University","funders":"","keywords":"Artificial neural network; Domain decomposition methods; Rate of convergence; Relaxation (psychology); Partial differential equation; Convergence (economics); Applied mathematics; Schwarz alternating method; Computation; Mathematics; Mathematical optimization; Dirichlet distribution; Acceleration; Computer science; Algorithm; Boundary value problem; Mathematical analysis; Key (lock); Finite element method; Artificial intelligence","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":[0.0005398708,0.0003316003,0.0005833282,0.0003471768,0.0003130146,0.0005714483,0.0007054046,0.0006785722,0.003066501],"category_scores_gemma":[0.001362374,0.0003023112,0.000409039,0.0003794227,0.0005259755,0.0007747388,0.000833226,0.001160821,0.0004994362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005174009,"about_ca_system_score_gemma":0.001153094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00348851,"about_ca_topic_score_gemma":0.004170704,"domain_scores_codex":[0.9998567,0.00006725919,0.00000613833,0.00001422823,0.00004125273,0.00001447726],"domain_scores_gemma":[0.9996356,0.0002044992,0.00002053934,0.00003251171,0.00007950234,0.00002746605],"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":[0.0001144538,0.00008518419,0.0003441216,0.0001720807,0.00005075725,0.00005855327,0.00006776705,0.7876253,0.005185571,0.1489196,0.003163837,0.0542128],"study_design_scores_gemma":[0.000002891174,0.000003017145,0.0000153048,0.000002112773,9.063018e-7,0.000002017693,0.000002096581,0.9948913,0.00009852283,0.004796576,0.0001841267,0.000001130131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01004776,0.0002155498,0.9866238,0.0002070267,0.00006160206,0.00002116541,0.00005416273,0.0000843331,0.002684481],"genre_scores_gemma":[0.3576466,0.0004964145,0.6336794,0.0002173698,0.00009671001,0.0002898953,0.0002566005,0.0002401784,0.007076916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00348851,"threshold_uncertainty_score":0.0102585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03746399663729461,"score_gpt":0.3323566542403746,"score_spread":0.29489265760308,"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."}}