{"id":"W2894841166","doi":"10.1016/j.mechmachtheory.2018.09.007","title":"Energy-leak monitoring and correction to enhance stability in the co-simulation of mechanical systems","year":2018,"lang":"en","type":"article","venue":"Mechanism and Machine Theory","topic":"Modeling and Simulation Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"General Motors (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España","keywords":"Dissipation; Energy (signal processing); Process (computing); Co-simulation; Computer science; Stability (learning theory); Component (thermodynamics); Mechanical system; Coupling (piping); System dynamics; Complex system; Control engineering; Simulation; Control theory (sociology); Engineering; Mechanical engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004925064,0.0004586186,0.0006098569,0.0004284861,0.0004317204,0.0007621076,0.0007315219,0.0006512149,0.001873406],"category_scores_gemma":[0.00236393,0.0002265866,0.0003210305,0.0002962671,0.0004085211,0.0009212045,0.001059508,0.0006226312,0.0002275739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003237166,"about_ca_system_score_gemma":0.0007164716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003158794,"about_ca_topic_score_gemma":0.002969947,"domain_scores_codex":[0.9997593,0.00007757152,0.00001687525,0.00004117129,0.00007612566,0.00002893322],"domain_scores_gemma":[0.9992923,0.0003189462,0.0000806784,0.00009495202,0.0001693276,0.00004393383],"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":[0.0002935774,0.0001478908,0.002478275,0.00005630812,0.000030183,0.00007147925,0.00009136399,0.9465283,0.01442206,0.004002537,0.0003902701,0.03148776],"study_design_scores_gemma":[0.000003839284,0.00001446966,0.000119101,0.000001411961,0.000002940135,0.000004879168,0.00000393232,0.9975375,0.001875122,0.000315518,0.0001195201,0.000001748163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1566856,0.0001212386,0.8376307,0.0002463323,0.00007352256,0.00007383525,0.00004318403,0.001218061,0.003907509],"genre_scores_gemma":[0.9809511,0.00002863166,0.01807809,0.00001592193,0.000005481274,0.00002768398,0.00001744003,0.00003249671,0.0008431539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003158794,"threshold_uncertainty_score":0.006280839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02453899024031918,"score_gpt":0.293843928468719,"score_spread":0.2693049382283998,"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."}}