2024/04/18 by Kislaya Ravi, Ravi, Kislaya, Vladyslav Fediukov +11 · 1 citation
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2404.11965
openalex publication_date 2024/04/18 · openalex created_date 2024/04/20 · openalex updated_date 2026/07/28
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.