Multifidelity Surrogate Models: A New Data Fusion Perspective

arXiv:2404.14456v1 Announce Type: new
Abstract: Multifidelity surrogate modelling combines data of varying accuracy and cost from different sources. It strategically uses low-fidelity models for rapid evaluations, saving computational resources, and high-fidelity models for detailed refinement. It improves decision-making by addressing uncertainties and surpassing the limits of single-fidelity models, which either oversimplify or are computationally intensive. Blending high-fidelity data for detailed responses with frequent low-fidelity data for quick approximations facilitates design optimisation in various domains.
Despite progress in interpolation, regression, enhanced …

abstract accuracy arxiv computational cost cs.lg cs.na data decision fidelity fusion low making math.na modelling perspective resources saving type