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

15 Jan 2019Yibo YangParis Perdikaris

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... (read more)

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