Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process
Zhao, Jiayi
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https://hdl.handle.net/2142/127512
Description
Title
Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process
Author(s)
Zhao, Jiayi
Issue Date
2024-12-12
Director of Research (if dissertation) or Advisor (if thesis)
Wang, Pingfeng
Department of Study
Industrial&Enterprise Sys Eng
Discipline
Industrial Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Surrogate Modeling
High Dimension
Autoencoder
Gaussian Process
Abstract
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.
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