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Computational Mechanics
Erschienen in: Computational Mechanics 2/2019

21.05.2019 | Original Paper

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

verfasst von: Yibo Yang, Paris Perdikaris

Erschienen in: Computational Mechanics | Ausgabe 2/2019

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Abstract

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. 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.
Vorheriger Artikel Parametric Gaussian process regression for big data
Nächster Artikel Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures

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Metadaten
Titel
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
verfasst von
Yibo Yang
Paris Perdikaris
Publikationsdatum
21.05.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Computational Mechanics / Ausgabe 2/2019
Print ISSN: 0178-7675
Elektronische ISSN: 1432-0924
DOI
https://doi.org/10.1007/s00466-019-01718-y

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