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

Yibo Yang, Paris Perdikaris
2019 arXiv   pre-print
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
more » ... rocesses. 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.
arXiv:1901.04878v1 fatcat:7vqb4qdrebdfpf5hlansajzgxa