Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
This addresses the challenge of modeling complex systems with noisy or variable data for researchers in computational science and engineering, though it appears incremental as it builds on existing variational inference methods.
The paper tackles the problem of constructing predictive surrogates for stochastic, high-dimensional, and multi-fidelity systems, resulting in models that can handle complex data and provide uncertainty-quantified predictions.
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.