Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations

• URL: http://arxiv.org/abs/2010.13749v1
• Date: Mon, 26 Oct 2020 17:39:05 GMT
• Title: Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
• Authors: Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh and Jayaraman J. Thiagarajan
• Abstract summary: Deep neural networks (DNNs) can serve as highly-accurate surrogate models. Prediction uncertainty estimates are crucial for making such comparisons meaningful.
• Score: 34.03414786863526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale numerical simulations are used across many scientific disciplines to facilitate experimental development and provide insights into underlying physical processes, but they come with a significant computational cost. Deep neural networks (DNNs) can serve as highly-accurate surrogate models, with the capacity to handle diverse datatypes, offering tremendous speed-ups for prediction and many other downstream tasks. An important use-case for these surrogates is the comparison between simulations and experiments; prediction uncertainty estimates are crucial for making such comparisons meaningful, yet standard DNNs do not provide them. In this work we define the fundamental requirements for a DNN to be useful for scientific applications, and demonstrate a general variational inference approach to equip predictions of scalar and image data from a DNN surrogate model trained on inertial confinement fusion simulations with calibrated Bayesian uncertainties. Critically, these uncertainties are interpretable, meaningful and preserve physics-correlations in the predicted quantities.

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