{"id":"W2997651305","doi":"10.1007/s00366-019-00909-8","title":"Recovering a moving boundary from Cauchy data in an inverse problem which arises in modeling brain tumor treatment: the (quasi)linearization idea combined with radial basis functions (RBFs) approximation","year":2020,"lang":"en","type":"article","venue":"Engineering With Computers","topic":"Numerical methods in engineering","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Linearization; Cauchy distribution; Radial basis function; Boundary (topology); Mathematics; Inverse; Applied mathematics; Boundary value problem; Domain (mathematical analysis); Inverse problem; Stability (learning theory); Mathematical analysis; Mathematical optimization; Nonlinear system; Computer science; Artificial neural network; Artificial intelligence; Geometry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002740048,0.0004451895,0.0004545286,0.0002632116,0.00008597944,0.0002080534,0.0004601929,0.00008452303,0.000004003922],"category_scores_gemma":[0.0001518282,0.0003782583,0.00003095667,0.001368251,0.00002376888,0.0009819451,0.000105311,0.0005098255,0.000002725591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003750226,"about_ca_system_score_gemma":0.00007318897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003098679,"about_ca_topic_score_gemma":0.0004677173,"domain_scores_codex":[0.9981005,0.0000854336,0.0004861986,0.0006022695,0.0002665787,0.0004590751],"domain_scores_gemma":[0.9987062,0.0003971927,0.00006234649,0.0006226567,0.00003829414,0.0001732657],"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":[0.00009541018,0.00004508451,0.001371511,0.0001047129,0.00007841047,0.00002493717,0.002017148,0.9908564,0.000943621,0.00001851487,0.00001127894,0.004432968],"study_design_scores_gemma":[0.001478007,0.0002994424,0.0008459976,0.00028336,0.00003695463,0.000007343581,0.0002121779,0.996057,0.0001584075,0.00001547948,0.0001481769,0.0004576755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3013934,0.00005800636,0.6970573,0.0002127999,0.0002374358,0.0004665495,0.00003500696,0.0005264814,0.00001295232],"genre_scores_gemma":[0.6998569,0.0000104207,0.2993742,0.00006505972,0.0001910584,0.00009659497,0.0002790071,0.0001259307,8.554499e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3984634,"threshold_uncertainty_score":0.999867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331835478949288,"score_gpt":0.2206499354334603,"score_spread":0.1973315806439674,"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."}}