{"id":"W2137499787","doi":"10.1002/aic.14152","title":"Order‐reduction of parabolic PDEs with time‐varying domain using empirical eigenfunctions","year":2013,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Eigenfunction; Parabolic partial differential equation; Mathematics; Partial differential equation; Domain (mathematical analysis); Mathematical analysis; Reduction (mathematics); Fictitious domain method; Nonlinear system; Time domain; Invariant (physics); Elliptic partial differential equation; Applied mathematics; Geometry; Computer science; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009696231,0.0001148007,0.0001619364,0.00007591767,0.0002567884,0.00006239844,0.000070954,0.00003521437,0.002674332],"category_scores_gemma":[0.000001901268,0.00008478638,0.00007581647,0.000248844,0.00005929545,0.000311868,0.00001585638,0.0003053167,0.00004780819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002088399,"about_ca_system_score_gemma":0.000085629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006268491,"about_ca_topic_score_gemma":2.400915e-7,"domain_scores_codex":[0.9992049,0.00006673222,0.0002452855,0.0001173691,0.0001587729,0.0002069303],"domain_scores_gemma":[0.9994052,0.00001774178,0.0001846487,0.0001072474,0.0001649852,0.0001201843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004309187,0.001644124,0.1401517,0.00004201787,0.001643294,0.000009794061,0.003872326,0.3106948,0.339829,0.003266493,0.05685206,0.1415635],"study_design_scores_gemma":[0.01579947,0.002741947,0.0477973,0.001255069,0.0017002,0.006565789,0.0128495,0.6562233,0.09393565,0.1129078,0.04333036,0.00489363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9485543,0.00005133931,0.04885924,0.000373844,0.0002023204,0.00009861002,0.000001241442,0.00001237715,0.001846751],"genre_scores_gemma":[0.9917385,0.000007096645,0.006735559,0.00003495771,0.0009616927,0.000005285975,0.000003525965,0.00001587603,0.0004974962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3455285,"threshold_uncertainty_score":0.9982374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02824055671308403,"score_gpt":0.2788209717012958,"score_spread":0.2505804149882118,"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."}}