{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/85125"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/85125","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"On-Line Monitoring, Control, and Reliability of Structural Dynamical Systems","abstract":"\"Ideas for improving the efficiency of Monte Carlo simulation (MCS) is the subject of the final section. Determining the low failure probabilities of typical engineering systems is quite difficult without using millions of MCS realizations to characterize the probability distribution. Several links between some MCS variance reduction techniques and Genetic Algorithms are discussed. A simple example, incorporating Genetic Algorithm operators into MCS, is shown to estimate probabilities a couple orders of magnitude smaller than standard MCS. Several concepts for characterizing the sense of a realization's \"\"importance\"\", such as discrepancy sensitivity and phase space velocity, are examined and found to successfully quantify realization importance.\"","abstract_html":""Ideas for improving the efficiency of Monte Carlo simulation (MCS) is the subject of the final section. Determining the low failure probabilities of typical engineering systems is quite difficult without using millions of MCS realizations to characterize the probability distribution. Several links between some MCS variance reduction techniques and Genetic Algorithms are discussed. A simple example, incorporating Genetic Algorithm operators into MCS, is shown to estimate probabilities a couple orders of magnitude smaller than standard MCS. Several concepts for characterizing the sense of a realization's ""importance"", such as discrepancy sensitivity and phase space velocity, are examined and found to successfully quantify realization importance."","abstract_has_math":false,"creators":["Johnson, Erik Arthur"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Bergman, Lawrence A.","Voulgaris, Petros G."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T22:34:30Z","date_published":"2015-09-25T22:34:30Z","updated_at":"2026-07-22T22:26:24Z","subjects":["Applied Mechanics"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9812643"],"render_values":[{"text":"(MiAaPQ)AAI9812643","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/85125","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bergman, Lawrence A.","Voulgaris, Petros G."]},{"key":"dc:creator","label":"Author","values":["Johnson, Erik Arthur"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T22:34:30Z","10000-01-01","1997"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied Mechanics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/85125","(MiAaPQ)AAI9812643"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"Ideas for improving the efficiency of Monte Carlo simulation (MCS) is the subject of the final section. 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Determining the low failure probabilities of typical engineering systems is quite difficult without using millions of MCS realizations to characterize the probability distribution. Several links between some MCS variance reduction techniques and Genetic Algorithms are discussed. A simple example, incorporating Genetic Algorithm operators into MCS, is shown to estimate probabilities a couple orders of magnitude smaller than standard MCS. Several concepts for characterizing the sense of a realization's \"\"importance\"\", such as discrepancy sensitivity and phase space velocity, are examined and found to successfully quantify realization importance.\"","Made available in DSpace on 2015-09-25T22:34:30Z (GMT). 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