{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127235"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127235","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adaptive computing for optimizing high-fidelity simulation runtimes","abstract":"The student, Janelle Domantay, submitted this Thesis for approval on 2024-11-28 at 07:41.","abstract_html":"The student, Janelle Domantay, submitted this Thesis for approval on 2024-11-28 at 07:41.","abstract_has_math":false,"creators":["Domantay, Janelle"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Driggs-Campbell, Katherine"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-04","date_published":"2024-12-04","updated_at":"2026-07-22T22:25:03Z","subjects":["Energy Modeling","Adaptive Computing","Simulation","Sustainability","Optimization"],"languages":["en","eng"],"rights":["Copyright 2024 Janelle Domantay"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127235","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Driggs-Campbell, Katherine"]},{"key":"dc:creator","label":"Author","values":["Domantay, Janelle"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-04","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Energy Modeling","Adaptive Computing","Simulation","Sustainability","Optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Janelle Domantay"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127235"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Janelle Domantay, submitted this Thesis for approval on 2024-11-28 at 07:41.","This Thesis was approved for publication on 2024-12-04 at 12:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21420 on 2025-03-28 at 14:27:19","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Janelle Domantay, accepted the attached license on 2024-11-28 at 07:33.","Physics-based and high-fidelity simulations are often leveraged to predict real-world trends and optimize resource consumption. However, these simulations are often computationally expensive and time consuming. Alternatively, small scale simulations can be performed at a fraction of the cost, but generate a host of scalability issues when translated to large-scale applications. To address model scalability issues, it is necessary to identify cost efficient methods for running physics-based models.Here we demonstrate how adaptive computing can be leveraged to create surrogate models that accurately approximate high-fidelity simulation behavior at a reduced runtime. We introduce a pipeline for training surrogate models that reaffirms the effectiveness of low-fidelity simulations. We elaborate on this pipeline for multi-scale simulations which demonstrate how adaptive computing can be used to stagger high-fidelity queries during surrogate training.These implementations demonstrate how adaptive computing can be used to manipulate model run-time and increase accuracy with reduced computational budgets.These implementations can be leveraged in any existing modeling pipeline regardless of discipline."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Adaptive computing for optimizing high-fidelity simulation runtimes"]}]}],"canonical_facts":{"dc:contributor":["Driggs-Campbell, Katherine"],"dc:creator":["Domantay, Janelle"],"dc:date":["2024-12-04","2024-12"],"dc:description":["The student, Janelle Domantay, submitted this Thesis for approval on 2024-11-28 at 07:41.","This Thesis was approved for publication on 2024-12-04 at 12:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21420 on 2025-03-28 at 14:27:19","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Janelle Domantay, accepted the attached license on 2024-11-28 at 07:33.","Physics-based and high-fidelity simulations are often leveraged to predict real-world trends and optimize resource consumption. However, these simulations are often computationally expensive and time consuming. Alternatively, small scale simulations can be performed at a fraction of the cost, but generate a host of scalability issues when translated to large-scale applications. To address model scalability issues, it is necessary to identify cost efficient methods for running physics-based models.Here we demonstrate how adaptive computing can be leveraged to create surrogate models that accurately approximate high-fidelity simulation behavior at a reduced runtime. We introduce a pipeline for training surrogate models that reaffirms the effectiveness of low-fidelity simulations. We elaborate on this pipeline for multi-scale simulations which demonstrate how adaptive computing can be used to stagger high-fidelity queries during surrogate training.These implementations demonstrate how adaptive computing can be used to manipulate model run-time and increase accuracy with reduced computational budgets.These implementations can be leveraged in any existing modeling pipeline regardless of discipline."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127235"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Janelle Domantay"],"dc:subject":["Energy Modeling","Adaptive Computing","Simulation","Sustainability","Optimization"],"dc:title":["Adaptive computing for optimizing high-fidelity simulation runtimes"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}