Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
<|reference_start|>Multi-fidelity Gaussian process surrogate modeling for regression problems in physics: 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.<|reference_end|>
@article{ravi2024multi-fidelity,
    title={Multi-fidelity Gaussian process surrogate modeling for regression
  problems in physics},
    author={Kislaya Ravi, Vladyslav Fediukov, Felix Dietrich, Tobias Neckel,
  Fabian Buse, Michael Bergmann, Hans-Joachim Bungartz},
    journal={arXiv preprint arXiv:2404.11965},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11965},
    primaryClass={stat.ML cs.LG physics.data-an}
}