{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129346"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129346","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"KL divergence – based disagreement sampling for multi-fidelity Bayesian optimization","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Iyer, Abhishek"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Smart, Jordan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-09","date_published":"2025-05-09","updated_at":"2026-07-22T22:25:05Z","subjects":["Active Learning","Bayesian Optimization","Multi-fidelity","Airfoil"],"languages":["en","eng"],"rights":["Copyright 2025 Abhishek Iyer"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129346","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Smart, Jordan"]},{"key":"dc:creator","label":"Author","values":["Iyer, Abhishek"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-09","2025-05"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Active Learning","Bayesian Optimization","Multi-fidelity","Airfoil"]}]},{"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 2025 Abhishek Iyer"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129346"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Abhishek Iyer, accepted the attached license on 2025-05-08 at 18:08.","The student, Abhishek Iyer, submitted this Thesis for approval on 2025-05-08 at 18:13.","This Thesis was approved for publication on 2025-05-09 at 12:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22271 on 2025-10-19 at 18:13:57","Computational costs have increased tremendously over time owing to the increase in complexity of design. The motivation to reduce costs has led to the adoption of multi-fidelity optimization techniques. Active Learning has gained prominence as a sampling method which allows for maximum optimization of the design problem while utilizing minimal available data, consequently decreasing the cost of computation. This thesis proposes a novel KL-Divergence sampling strategy which iteratively makes use of fidelities to sample through a design space to optimize according to a target objective. This method was evaluated using the Rastrigin function and then applied on a design space consisting of 4-digit NACA airfoil combinations. It is observed that the KL divergence method provided a better optimization result compared to the Least Confidence method while simultaneously reducing reliance on information from higher fidelities. The disagreement sampler iterated over the airfoil design space and provided a better result as compared to the Least Confidence method."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["KL divergence – based disagreement sampling for multi-fidelity Bayesian optimization"]}]}],"canonical_facts":{"dc:contributor":["Smart, Jordan"],"dc:creator":["Iyer, Abhishek"],"dc:date":["2025-05-09","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Abhishek Iyer, accepted the attached license on 2025-05-08 at 18:08.","The student, Abhishek Iyer, submitted this Thesis for approval on 2025-05-08 at 18:13.","This Thesis was approved for publication on 2025-05-09 at 12:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22271 on 2025-10-19 at 18:13:57","Computational costs have increased tremendously over time owing to the increase in complexity of design. The motivation to reduce costs has led to the adoption of multi-fidelity optimization techniques. Active Learning has gained prominence as a sampling method which allows for maximum optimization of the design problem while utilizing minimal available data, consequently decreasing the cost of computation. This thesis proposes a novel KL-Divergence sampling strategy which iteratively makes use of fidelities to sample through a design space to optimize according to a target objective. This method was evaluated using the Rastrigin function and then applied on a design space consisting of 4-digit NACA airfoil combinations. It is observed that the KL divergence method provided a better optimization result compared to the Least Confidence method while simultaneously reducing reliance on information from higher fidelities. The disagreement sampler iterated over the airfoil design space and provided a better result as compared to the Least Confidence method."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129346"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Abhishek Iyer"],"dc:subject":["Active Learning","Bayesian Optimization","Multi-fidelity","Airfoil"],"dc:title":["KL divergence – based disagreement sampling for multi-fidelity Bayesian optimization"],"dc:type":["text"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}