Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要