{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127512"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127512","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_has_math":false,"creators":["Zhao, Jiayi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Wang, Pingfeng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-12","date_published":"2024-12-12","updated_at":"2026-07-22T22:25:04Z","subjects":["Surrogate Modeling","High Dimension","Autoencoder","Gaussian Process"],"languages":["en","eng"],"rights":["Copyright 2024 Jiayi Zhao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127512","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Pingfeng"]},{"key":"dc:creator","label":"Author","values":["Zhao, Jiayi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-12","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial 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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Surrogate Modeling","High Dimension","Autoencoder","Gaussian Process"]}]},{"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 Jiayi Zhao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127512"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Jiayi Zhao, accepted the attached license on 2024-12-09 at 09:42.","The student, Jiayi Zhao, submitted this Thesis for approval on 2024-12-09 at 09:50.","This Thesis was approved for publication on 2024-12-12 at 09:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21546 on 2025-03-28 at 14:57:05","High-dimensional surrogate modeling poses significant challenges, particularly when data is limited, as traditional Gaussian Process (GP) models struggle with scalability and computational efficiency. This paper addresses these issues by proposing a framework for optimizing the latent dimension in an Autoencoder-Gaussian Process (AE-GP) model, ensuring both accuracy and scalability. Using 10 representative benchmark functions, the study evaluates the GP’s performance in terms of Mean Squared Error (MSE) under 5-fold cross-validation, with latent dimensions ranging from 1 to 20. The experiments are conducted across varying combinations of dataset dimensions D0 and sample sizes N, identifying the best-performing specific values and ranges of latent dimensions. These optimal dimensions are then applied to high-dimensional case studies with unknown x-y relationships to validate the model’s practical applicability. By proposing an adaptive framework for high-dimensional surrogate modeling, this work provides actionable insights for selecting AE latent dimensions under resource constraints and demonstrates its effectiveness in improving model scalability and accuracy across diverse scenarios."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process"]}]}],"canonical_facts":{"dc:contributor":["Wang, Pingfeng"],"dc:creator":["Zhao, Jiayi"],"dc:date":["2024-12-12","2024-12"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Jiayi Zhao, accepted the attached license on 2024-12-09 at 09:42.","The student, Jiayi Zhao, submitted this Thesis for approval on 2024-12-09 at 09:50.","This Thesis was approved for publication on 2024-12-12 at 09:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21546 on 2025-03-28 at 14:57:05","High-dimensional surrogate modeling poses significant challenges, particularly when data is limited, as traditional Gaussian Process (GP) models struggle with scalability and computational efficiency. This paper addresses these issues by proposing a framework for optimizing the latent dimension in an Autoencoder-Gaussian Process (AE-GP) model, ensuring both accuracy and scalability. Using 10 representative benchmark functions, the study evaluates the GP’s performance in terms of Mean Squared Error (MSE) under 5-fold cross-validation, with latent dimensions ranging from 1 to 20. The experiments are conducted across varying combinations of dataset dimensions D0 and sample sizes N, identifying the best-performing specific values and ranges of latent dimensions. These optimal dimensions are then applied to high-dimensional case studies with unknown x-y relationships to validate the model’s practical applicability. By proposing an adaptive framework for high-dimensional surrogate modeling, this work provides actionable insights for selecting AE latent dimensions under resource constraints and demonstrates its effectiveness in improving model scalability and accuracy across diverse scenarios."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127512"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Jiayi Zhao"],"dc:subject":["Surrogate Modeling","High Dimension","Autoencoder","Gaussian Process"],"dc:title":["Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}