{"id":"W2268582187","doi":"10.48550/arxiv.1509.04693","title":"Well Control Optimization using Derivative-Free Algorithms and a Multiscale Approach","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science and Technology Major Project","keywords":"Mathematical optimization; CMA-ES; Computer science; Regularization (linguistics); Stochastic optimization; A priori and a posteriori; Algorithm; Optimization problem; Particle swarm optimization; Evolution strategy; Mathematics; Evolutionary algorithm; 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":[0.001226815,0.0008385864,0.0009161016,0.000654716,0.0003823501,0.001223297,0.0008602722,0.001242374,0.001061335],"category_scores_gemma":[0.002781449,0.0005419819,0.0008561507,0.0005492668,0.001013263,0.001206057,0.001070198,0.001018047,0.0001914455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008594016,"about_ca_system_score_gemma":0.001055888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004269901,"about_ca_topic_score_gemma":0.002911126,"domain_scores_codex":[0.9995953,0.0001375151,0.00002587399,0.00007914834,0.000136183,0.0000259523],"domain_scores_gemma":[0.999184,0.0005127641,0.0001028648,0.00008230168,0.00009288616,0.00002511707],"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":[0.00001416363,0.00001680964,0.0002502849,0.00004148322,0.00002808226,0.00002285226,0.00002424567,0.9656342,0.002148343,0.01493532,0.0001623389,0.0167219],"study_design_scores_gemma":[0.000002694722,0.000007126719,0.00003014213,0.000002642195,0.000002171635,0.000002962829,0.000001546382,0.9979063,0.0001976182,0.00165464,0.0001896008,0.000002548288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00714649,0.0002055166,0.9906806,0.0001193449,0.00002041665,0.00002123459,0.000009602273,0.0001015263,0.001695351],"genre_scores_gemma":[0.4396823,0.0004980333,0.5569614,0.0001505253,0.00006320034,0.0001988669,0.00005120506,0.0001227842,0.002271625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004269901,"threshold_uncertainty_score":0.008490086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08246654947435707,"score_gpt":0.2085270204111088,"score_spread":0.1260604709367517,"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."}}