{"id":"W4389662006","doi":"10.2118/218386-pa","title":"A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification","year":2023,"lang":"en","type":"article","venue":"SPE Journal","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolutional neural network; Robustness (evolution); Computer science; Generalization; Artificial intelligence; Deep learning; Uncertainty quantification; Machine learning; Artificial neural network; Reservoir computing; Recurrent neural network; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004238102,0.0004136176,0.0003929622,0.0002735036,0.0002508175,0.0005123385,0.0007036438,0.0007170454,0.0009678598],"category_scores_gemma":[0.001182166,0.000315002,0.0004727001,0.000381547,0.0004395372,0.0008327886,0.0007837774,0.0009676169,0.0001354508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007297293,"about_ca_system_score_gemma":0.001208934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01159347,"about_ca_topic_score_gemma":0.0077691,"domain_scores_codex":[0.9998673,0.0000316091,0.000008144805,0.00003093901,0.00004383336,0.00001810059],"domain_scores_gemma":[0.9997099,0.0001286643,0.00003929335,0.00002503685,0.00008079528,0.00001624359],"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":[0.000008602363,0.000005232751,0.0001631436,0.000007117023,0.000005607041,0.00001237624,0.000003441738,0.9929076,0.0005930858,0.001435849,0.0001172331,0.0047406],"study_design_scores_gemma":[2.377313e-7,8.857978e-7,0.00001347124,3.882799e-7,4.266202e-7,7.84989e-7,2.806828e-7,0.9995767,0.00007205766,0.0003080472,0.00002612696,5.067936e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0731998,0.000308214,0.9207526,0.0004380681,0.00006103367,0.00002741768,0.0001794827,0.0004033793,0.004630016],"genre_scores_gemma":[0.9318042,0.0001938582,0.0650901,0.00008236327,0.00002768708,0.00008331293,0.0001972678,0.00004270143,0.002478515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01159347,"threshold_uncertainty_score":0.02305198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06555054439383073,"score_gpt":0.3381210081001814,"score_spread":0.2725704637063506,"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."}}