KL divergence – based disagreement sampling for multi-fidelity Bayesian optimization

Iyer, Abhishek

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https://hdl.handle.net/2142/129346 Copy

Description

Title

KL divergence – based disagreement sampling for multi-fidelity Bayesian optimization

Author(s)

Iyer, Abhishek

Issue Date

2025-05-09

Director of Research (if dissertation) or Advisor (if thesis)

Smart, Jordan

Department of Study

Aerospace Engineering

Discipline

Aerospace Engineering

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Active Learning
• Bayesian Optimization
• Multi-Fidelity
• Airfoil

Abstract

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.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129346

Copyright and License Information

Copyright 2025 Abhishek Iyer

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