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

arXiv:2404.11965v1 Announce Type: cross
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 …

abstract arxiv availability challenges constraints cost cs.lg data error fidelity modeling physics physics.data-an process regression simulations solution stat.ml type