MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources
Abstract
We present an approach for constructing a surrogate from ensembles of information sources of varying cost and accuracy. The multifidelity surrogate encodes connections between information sources as a directed acyclic graph, and is trained via gradient-based minimization of a nonlinear least squares objective. While the vast majority of state-of-the-art assumes hierarchical connections between information sources, our approach works with...
Paper Details
Title
MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources
Published Date
Aug 17, 2021
Journal
Volume
68
Issue
4
Pages
741 - 758
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