{"id":"W4406998460","doi":"10.1016/j.ress.2025.110866","title":"A systematic resilience assessment framework for multi-state systems based on physics-informed neural network","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"Petro-Canada","funders":"China University of Petroleum, Beijing; National Natural Science Foundation of China","keywords":"Resilience (materials science); Artificial neural network; State (computer science); Computer science; Systems engineering; Management science; Artificial intelligence; Engineering; Physics; Algorithm","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":[0.002009876,0.001319202,0.001090268,0.001885252,0.0006191998,0.001165383,0.001835307,0.001137167,0.001881878],"category_scores_gemma":[0.003508475,0.0005838299,0.001041205,0.000888187,0.001089487,0.002489944,0.002461796,0.001346995,0.0002434194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224778,"about_ca_system_score_gemma":0.002725037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009097627,"about_ca_topic_score_gemma":0.01105417,"domain_scores_codex":[0.9993161,0.0002099275,0.0000395261,0.0001338175,0.0002282906,0.00007231074],"domain_scores_gemma":[0.9990068,0.000428653,0.0001260988,0.00007806969,0.0003023611,0.000057971],"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":[0.0000105084,0.00001822301,0.0002830915,0.00005111502,0.0000475963,0.00003817379,0.00002143525,0.9648235,0.0006808255,0.02156335,0.0003534354,0.01210874],"study_design_scores_gemma":[0.000001392324,0.000006701939,0.00006110224,0.000007030957,0.00000692814,0.000004878355,0.000003053449,0.9905133,0.00009254848,0.009185489,0.0001137703,0.000003773082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004318982,0.000203901,0.9936426,0.0001545582,0.00001574139,0.00004694834,0.00005253864,0.0001423213,0.001422322],"genre_scores_gemma":[0.6522859,0.0009214253,0.3421645,0.0001688878,0.0001253473,0.000569999,0.0003590347,0.0001398142,0.003265215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009097627,"threshold_uncertainty_score":0.01808935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007810988837815542,"score_gpt":0.2628894575778986,"score_spread":0.2550784687400831,"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."}}