2026-09-19T11:58:56Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/127512 2026-02-03 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

application/pdf en text 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 Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process Zhao, Jiayi 2024-12-12 Wang, Pingfeng Surrogate Modeling High Dimension Autoencoder Gaussian Process eng 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. 2024-12 Thesis https://hdl.handle.net/2142/127512 Copyright 2024 Jiayi Zhao Industrial&Enterprise Sys Eng Industrial Engineering University of Illinois at Urbana-Champaign M.S. Thesis