Multi-fidelity Gaussian process surrogate modeling for regression problems in physics

• URL: http://arxiv.org/abs/2404.11965v2
• Date: Mon, 29 Jul 2024 14:43:48 GMT
• Title: Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
• Authors: Kislaya Ravi, Vladyslav Fediukov, Felix Dietrich, Tobias Neckel, Fabian Buse, Michael Bergmann, Hans-Joachim Bungartz,
• Abstract summary: Multi-fidelity methods provide a solution by chaining models in a hierarchy with increasing fidelity. We show that multi-fidelity methods generally have a smaller prediction error for the same computational cost as compared to the single-fidelity method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the main challenges in surrogate modeling is the limited availability of data due to resource constraints associated with computationally expensive simulations. Multi-fidelity methods provide a solution by chaining models in a hierarchy with increasing fidelity, associated with lower error, but increasing cost. In this paper, we compare different multi-fidelity methods employed in constructing Gaussian process surrogates for regression. Non-linear autoregressive methods in the existing literature are primarily confined to two-fidelity models, and we extend these methods to handle more than two levels of fidelity. Additionally, we propose enhancements for an existing method incorporating delay terms by introducing a structured kernel. We demonstrate the performance of these methods across various academic and real-world scenarios. Our findings reveal that multi-fidelity methods generally have a smaller prediction error for the same computational cost as compared to the single-fidelity method, although their effectiveness varies across different scenarios.

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