{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/80948"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/80948","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Hybrid Machine Learning Approach for Predictive Modeling of Complex Systems","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Singh, Shubhendu Kumar; 0000-0003-1447-8120"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Rai, Rahul","Mechanical and Aerospace Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10-29T16:48:25Z","date_published":"2019-10-29T16:48:25Z","updated_at":"2026-07-27T19:05:28Z","subjects":["mechanical engineering","artificial intelligence"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/80948","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rai, Rahul","Mechanical and Aerospace Engineering"]},{"key":"dc:creator","label":"Author","values":["Singh, Shubhendu Kumar; 0000-0003-1447-8120"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-10-29T16:48:25Z","2019","2019-08-09 15:07:44"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mechanical engineering","artificial intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/80948"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","We introduce a novel machine learning-based fusion model, termed as PI-LSTM (Physics-Infused Long Short-Term Memory Networks) that integrates first principle Physics-Based Models and Long Short-Term Memory (LSTM) network. Our architecture aims at combining equation-based models with data-driven machine learning models to enable accurate predictions of complex dynamic systems. In this hybrid architecture, recurrency aids the temporal memory of the inputs and output of the partial physics model, in a way that facilitates generalization with scarce data sets. We illustrate the application of PI-LSTM on two dynamical systems namely Inverted Pendulum and Tumor Growth. Empirical results on both test problems stand witness to the effectiveness of using physics in guiding machine learning models and the superiority of the outlined hybrid model over purely data-driven models."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Hybrid Machine Learning Approach for Predictive Modeling of Complex Systems"]}]}],"canonical_facts":{"dc:contributor":["Rai, Rahul","Mechanical and Aerospace Engineering"],"dc:creator":["Singh, Shubhendu Kumar; 0000-0003-1447-8120"],"dc:date":["2019-10-29T16:48:25Z","2019","2019-08-09 15:07:44"],"dc:description":["M.S.","We introduce a novel machine learning-based fusion model, termed as PI-LSTM (Physics-Infused Long Short-Term Memory Networks) that integrates first principle Physics-Based Models and Long Short-Term Memory (LSTM) network. Our architecture aims at combining equation-based models with data-driven machine learning models to enable accurate predictions of complex dynamic systems. In this hybrid architecture, recurrency aids the temporal memory of the inputs and output of the partial physics model, in a way that facilitates generalization with scarce data sets. We illustrate the application of PI-LSTM on two dynamical systems namely Inverted Pendulum and Tumor Growth. Empirical results on both test problems stand witness to the effectiveness of using physics in guiding machine learning models and the superiority of the outlined hybrid model over purely data-driven models."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/80948"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["mechanical engineering","artificial intelligence"],"dc:title":["Hybrid Machine Learning Approach for Predictive Modeling of Complex Systems"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:28Z"}