{"id":"W2608303398","doi":"10.1063/1.4980864","title":"Application of implicit scheme with AGE iterative method for solving fuzzy parabolic equation","year":2017,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"AGE-WELL","keywords":"Discretization; Iterative method; Fuzzy logic; Mathematics; Alternating direction implicit method; Gauss–Seidel method; Applied mathematics; Scheme (mathematics); Mathematical optimization; Successive over-relaxation; Computer science; Parabolic partial differential equation; Algorithm; Finite difference method; Local convergence; Mathematical analysis; Partial differential equation; Artificial intelligence","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":[0.0005178,0.0003061637,0.0003831241,0.0002911857,0.0003810252,0.0003409783,0.0006295735,0.0006786773,0.0007718193],"category_scores_gemma":[0.001124136,0.0001331544,0.0004519855,0.0002603894,0.0004780976,0.0005169427,0.0006626611,0.0006353738,0.0001245386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002763939,"about_ca_system_score_gemma":0.0007181318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002853176,"about_ca_topic_score_gemma":0.001965918,"domain_scores_codex":[0.9997632,0.00008470511,0.00001313589,0.00002369329,0.00009741283,0.00001788539],"domain_scores_gemma":[0.9997223,0.00011865,0.00002664496,0.00002687044,0.00009095067,0.00001447838],"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":[0.0001570997,0.00009325933,0.003051638,0.000359572,0.00007528776,0.0003763613,0.0006513563,0.6810596,0.0475409,0.137546,0.001098748,0.1279902],"study_design_scores_gemma":[0.000006689265,0.00002434569,0.0001097363,0.000006245412,0.000004026277,0.00004483615,0.00001179717,0.9933997,0.002051189,0.003089444,0.001246486,0.000005437017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03316063,0.0002957623,0.9625626,0.0001157452,0.00005563377,0.00003227977,0.00001033384,0.00005824944,0.003708732],"genre_scores_gemma":[0.5708255,0.0004316905,0.4238657,0.00006156127,0.00005293544,0.0001203492,0.00002870066,0.00003115412,0.004582476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002853176,"threshold_uncertainty_score":0.00567311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06852610763613955,"score_gpt":0.3453782114643532,"score_spread":0.2768521038282137,"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."}}