{"id":"W2254823475","doi":"10.1016/j.jcp.2016.02.024","title":"PDEs on moving surfaces via the closest point method and a modified grid based particle method","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Fluid Dynamics Simulations and Interactions","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Embedding; Grid; Material point method; Partial differential equation; Grid method multiplication; Mathematics; Regular grid; Surface (topology); Eulerian path; Convergence (economics); Range (aeronautics); Moving least squares; Algorithm; Mathematical analysis; Computer science; Geometry; Finite element method; Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001120647,0.0006526635,0.001342609,0.00122874,0.0006799058,0.001292067,0.002184171,0.002470172,0.00273717],"category_scores_gemma":[0.003675655,0.0005625804,0.001420314,0.0009783808,0.001461694,0.001989648,0.002477807,0.002160164,0.0006481542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006466277,"about_ca_system_score_gemma":0.000899006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004741715,"about_ca_topic_score_gemma":0.003126214,"domain_scores_codex":[0.9993379,0.0002419603,0.00002953073,0.00006716034,0.0002921182,0.00003138851],"domain_scores_gemma":[0.9988391,0.0005724881,0.00008811041,0.000127743,0.0002894169,0.00008319483],"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":[0.00008611297,0.0001039509,0.0006869912,0.0001595457,0.00007417818,0.0002479622,0.0001629021,0.5762768,0.005112287,0.3709426,0.002589543,0.0435571],"study_design_scores_gemma":[0.000009020913,0.00000848808,0.00005896843,0.000004129972,0.000003208118,0.00001818355,0.000006145583,0.983875,0.000180352,0.01474065,0.001088239,0.000007530099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007468031,0.0002533034,0.9887424,0.0001960058,0.0002118297,0.0000365358,0.00003375695,0.0000583792,0.002999882],"genre_scores_gemma":[0.2790077,0.0007739399,0.6991205,0.0002465197,0.0004177185,0.0002737913,0.000196157,0.0003437808,0.01961989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004741715,"threshold_uncertainty_score":0.009428263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727699935690299,"score_gpt":0.286759233553001,"score_spread":0.269482234196098,"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."}}