MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources
- Univ. of Michigan, Ann Arbor, MI (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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 flexibly structured information sources that may not admit a strict hierarchy. The formulation has two advantages: (1) increased data efficiency due to parsimonious multifidelity networks that can be tailored to the application; and (2) no constraints on the training data—we can combine noisy, non-nested evaluations of the information sources. Finally, numerical examples ranging from synthetic to physics-based computational mechanics simulations indicate the error in our approach can be orders-of-magnitude smaller, particularly in the low-data regime, than single-fidelity and hierarchical multifidelity approaches.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- NA0003525
- OSTI ID:
- 1820427
- Report Number(s):
- SAND-2021-10721J; 699125
- Journal Information:
- Computational Mechanics, Vol. 68, Issue 4; ISSN 0178-7675
- Publisher:
- SpringerCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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