{"id":"W4256091510","doi":"10.23952/jnva.4.2020.2.10","title":"Three optimization formulations for an inverse problem in saddle point problems with applications to elasticity imaging of locating tumor in incompressible medium","year":2020,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Ministerio de Ciencia, Innovación y Universidades; Agencia Estatal de Investigación; National Science Foundation","keywords":"Saddle point; Compressibility; Saddle; Elasticity (physics); Inverse problem; Inverse; Mathematics; Mathematical optimization; Mathematical analysis; Computer science; Applied mathematics; Physics; Geometry; Mechanics; Materials science; Composite material","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009102962,0.0001151311,0.0004375905,0.0004854939,0.00005491632,0.00002768877,0.0001432037,0.00003199029,0.00001967111],"category_scores_gemma":[0.0005805526,0.00009427649,0.0000830192,0.001574454,0.00002920029,0.0003356764,0.00004384407,0.0001586725,1.96958e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006180358,"about_ca_system_score_gemma":0.000132642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008531777,"about_ca_topic_score_gemma":0.0003429432,"domain_scores_codex":[0.9984118,0.00007134096,0.0009114802,0.0001846491,0.0002863621,0.0001344097],"domain_scores_gemma":[0.9979475,0.0006178911,0.0006879167,0.00009947703,0.0005135229,0.0001337441],"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":[0.0001119538,0.0002927809,0.04442244,0.0001646015,0.000176774,7.509812e-7,0.001016827,0.9492415,0.0004943782,0.003571645,0.000009786206,0.0004965786],"study_design_scores_gemma":[0.0007906377,0.0001654446,0.003305794,0.00008201293,0.0003537905,0.000003678038,0.0001852385,0.9521612,0.00009147037,0.04274229,0.00001533089,0.0001031002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01029538,0.00001129719,0.9879879,0.001130169,0.000007847369,0.0004977584,0.00002983066,0.000007069674,0.00003274529],"genre_scores_gemma":[0.1616011,0.000002267341,0.8381462,0.0001087876,0.00006569557,0.00004824887,0.00001489601,0.00001198918,8.644926e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1513057,"threshold_uncertainty_score":0.3844483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06593555589670458,"score_gpt":0.3409038667484315,"score_spread":0.274968310851727,"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."}}