{"id":"W4403993488","doi":"10.1016/j.compchemeng.2024.108898","title":"Physics-informed neural networks for state reconstruction of hydrogen energy transportation systems","year":2024,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Energy (signal processing); State (computer science); Hydrogen; Statistical physics; Hydrogen fuel; Computer science; Engineering; Physics; Artificial intelligence; Algorithm; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003444069,0.0001612344,0.0001940642,0.00005810086,0.00001250139,0.00003304841,0.0000822439,0.00006977866,8.806775e-7],"category_scores_gemma":[0.000003306853,0.0001747143,0.0001086002,0.0001727469,0.0000126877,0.0001587524,0.000005174965,0.0001057261,2.35138e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005818977,"about_ca_system_score_gemma":0.000008220877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006706775,"about_ca_topic_score_gemma":4.81269e-7,"domain_scores_codex":[0.9992629,0.000001986013,0.0002967077,0.0001369636,0.00007330724,0.0002280796],"domain_scores_gemma":[0.9996952,0.0001165737,0.00002197768,0.00008253614,0.00002089242,0.00006283407],"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":[0.000003511478,0.000001966055,0.000005026524,0.0004881761,0.00006736504,0.00000177857,0.0001012088,0.9396828,0.01891827,0.0006782074,0.00006899625,0.03998271],"study_design_scores_gemma":[0.0001248714,0.00001141288,0.000002084449,0.000304253,0.00001771768,0.00001055018,0.000003312109,0.9471738,0.05136188,0.0000269567,0.0007995199,0.000163674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2029795,0.0009474911,0.7935671,0.000002397357,0.001888184,0.00006323654,0.00001707991,0.000503039,0.00003199193],"genre_scores_gemma":[0.9965282,0.00002893727,0.002860768,0.00000302282,0.0004031272,0.00002528455,0.00009619172,0.00004859432,0.000005855211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7935488,"threshold_uncertainty_score":0.7124642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006454293136211651,"score_gpt":0.1788518532753872,"score_spread":0.1723975601391756,"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."}}