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Title: Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems

Journal Article · · Computational Mechanics
 [1]; ORCiD logo [2]
  1. Univ. of Pennsylvania, Philadelphia, PA (United States); University of Pennsylvania
  2. Univ. of Pennsylvania, Philadelphia, PA (United States)
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Here, we present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogate models on paired input–output observations that may be stochastic in nature, originate from different information sources of variable fidelity, or be corrupted by complex noise processes. The resulting surrogates can accommodate high-dimensional inputs and outputs and are able to return predictions with quantified uncertainty. Furthermore, the effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation in high-dimensional dynamical systems.

Research Organization:
Univ. of Pennsylvania, Philadelphia, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Grant/Contract Number:
SC0019116
OSTI ID:
1595804
Journal Information:
Computational Mechanics, Journal Name: Computational Mechanics Journal Issue: 2 Vol. 64; ISSN 0178-7675
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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