{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/83838"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/83838","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Boon of Hybrid Approaches over the Bane of Model Based and Machine Learning Approaches in Modelling Cyber-Physical Systems","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Sahu, Chandan Kumar; 0000-0001-5893-4031"],"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":2022,"date_issued":"2022-06-17T19:54:37Z","date_published":"2022-06-17T19:54:37Z","updated_at":"2026-07-27T19:05:28Z","subjects":["mechanical engineering","electrical engineering","computer science"],"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/10477/83838","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":["Sahu, Chandan Kumar; 0000-0001-5893-4031"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-17T19:54:37Z","2020"]},{"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","electrical engineering","computer science"]}]},{"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/10477/83838"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","Physics-based models (MB) and machine learning models (ML) have emerged as two paradigms for modeling any system. They represent the two opposite ends of the spectrum of system knowledge/data. Interpretability, tractability, and composability provided the thrust for MB. Genericity and accuracy drove ML. Interestingly, the drivers of physics models are the deterrents of machine learning models and vice versa. Even with their inherent boons and banes, they have evolved and practiced independently of each other for computing. Recently, several works attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research is scattered and unorganized. So, we make a meticulous attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we prepared a five-faceted generic framework for the comprehensive evaluation of hybrid learning models (MB + ML). Finally, we conclude by shedding some light on the challenges of hybrid models, which we as a research community should focus for expediting the research and harnessing the full potential of hybrid learning models. All of these have been discussed with a clear focus on modeling cyber-physical systems (CPS) and overcoming the limitations of the existing models of CPS."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Boon of Hybrid Approaches over the Bane of Model Based and Machine Learning Approaches in Modelling Cyber-Physical Systems"]}]}],"canonical_facts":{"dc:contributor":["Rai, Rahul","Mechanical and Aerospace Engineering"],"dc:creator":["Sahu, Chandan Kumar; 0000-0001-5893-4031"],"dc:date":["2022-06-17T19:54:37Z","2020"],"dc:description":["M.S.","Physics-based models (MB) and machine learning models (ML) have emerged as two paradigms for modeling any system. They represent the two opposite ends of the spectrum of system knowledge/data. Interpretability, tractability, and composability provided the thrust for MB. Genericity and accuracy drove ML. Interestingly, the drivers of physics models are the deterrents of machine learning models and vice versa. Even with their inherent boons and banes, they have evolved and practiced independently of each other for computing. Recently, several works attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research is scattered and unorganized. So, we make a meticulous attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we prepared a five-faceted generic framework for the comprehensive evaluation of hybrid learning models (MB + ML). Finally, we conclude by shedding some light on the challenges of hybrid models, which we as a research community should focus for expediting the research and harnessing the full potential of hybrid learning models. All of these have been discussed with a clear focus on modeling cyber-physical systems (CPS) and overcoming the limitations of the existing models of CPS."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/83838"],"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","electrical engineering","computer science"],"dc:title":["Boon of Hybrid Approaches over the Bane of Model Based and Machine Learning Approaches in Modelling Cyber-Physical Systems"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:28Z"}