{"id":"W3194202055","doi":"10.1016/j.cma.2022.114687","title":"The Neural Network shifted-proper orthogonal decomposition: A machine learning approach for non-linear reduction of hyperbolic equations","year":2022,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"H2020 European Research Council; European Research Council; Ministero dell’Istruzione, dell’Università e della Ricerca; CANDU Owners Group","keywords":"Linear subspace; Subspace topology; Benchmark (surveying); Projection (relational algebra); Artificial neural network; Algorithm; Field (mathematics); Advection; Computer science; Reduction (mathematics); Transformation (genetics); Range (aeronautics); Mathematics; Applied mathematics; Artificial intelligence; Mathematical optimization; Geometry; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007910402,0.0006302789,0.0006712676,0.000643051,0.0003058716,0.0007538649,0.0008538959,0.0005631855,0.002046065],"category_scores_gemma":[0.001838194,0.0003432223,0.0007297648,0.0006643729,0.0009501658,0.001504873,0.001213761,0.001754272,0.0006045313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003386345,"about_ca_system_score_gemma":0.0007717957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001024354,"about_ca_topic_score_gemma":0.001359774,"domain_scores_codex":[0.9997061,0.0001314051,0.00001571075,0.00004524026,0.00008084824,0.00002066836],"domain_scores_gemma":[0.9996561,0.0001226606,0.00003828243,0.00006339751,0.00008728106,0.00003242945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000893155,0.00008719869,0.0004710007,0.0002739858,0.0000572946,0.00006896754,0.0001324868,0.1970582,0.009202586,0.6483896,0.004354219,0.1398152],"study_design_scores_gemma":[0.00000529112,0.00001515937,0.0001022146,0.000008846715,0.000004996917,0.0000130953,0.000007306147,0.8992286,0.0006526115,0.09821851,0.001735777,0.000007551648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006741241,0.000316861,0.9910042,0.0001396592,0.00008788655,0.00002088108,0.00004587784,0.00006094189,0.001582507],"genre_scores_gemma":[0.226476,0.001983163,0.7562177,0.0002697449,0.0004128556,0.0001775824,0.0003577181,0.0003944352,0.01371093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002046065,"threshold_uncertainty_score":0.006844819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02508878470345987,"score_gpt":0.2863297057448698,"score_spread":0.2612409210414099,"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."}}