Partitioned Surrogates and Thompson Sampling for Multidisciplinary Bayesian Optimization
The long runtime associated with simulating multidisciplinary systems challenges the use of Bayesian optimization for multidisciplinary design optimization (MDO). This is particularly the case if the coupled system is modeled in a partitioned manner and feedback loops, known as strong coupling, are...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
16.08.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The long runtime associated with simulating multidisciplinary systems
challenges the use of Bayesian optimization for multidisciplinary design
optimization (MDO). This is particularly the case if the coupled system is
modeled in a partitioned manner and feedback loops, known as strong coupling,
are present. This work introduces a method for Bayesian optimization in MDO
called "Multidisciplinary Design Optimization using Thompson Sampling",
abbreviated as MDO-TS. Instead of replacing the whole system with a surrogate,
we substitute each discipline with such a Gaussian process. Since an entire
multidisciplinary analysis is no longer required for enrichment, evaluations
can potentially be saved. However, the objective and associated uncertainty are
no longer analytically estimated. Since most adaptive sampling strategies
assume the availability of these estimates, they cannot be applied without
modification. Thompson sampling does not require this explicit availability.
Instead, Thompson sampling balances exploration and exploitation by selecting
actions based on optimizing random samples from the objective. We combine
Thompson sampling with an approximate sampling strategy that uses random
Fourier features. This approach produces continuous functions that can be
evaluated iteratively. We study the application of this infill criterion to
both an analytical problem and the shape optimization of a simple
fluid-structure interaction example. |
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DOI: | 10.48550/arxiv.2408.08691 |