Learning and correcting non-Gaussian model errors
Smyl, Danny; Tallman, Tyler N.; Black, Jonathan A.; Hauptmann, Andreas; Liu, Dong (2021-01-29)
Danny Smyl, Tyler N. Tallman, Jonathan A. Black, Andreas Hauptmann, Dong Liu, Learning and correcting non-Gaussian model errors, Journal of Computational Physics, Volume 432, 2021, 110152, ISSN 0021-9991, https://doi.org/10.1016/j.jcp.2021.110152
© 2021 Elsevier Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe202102053799
Tiivistelmä
Abstract
All discretized numerical models contain modeling errors — this reality is amplified when reduced-order models are used. The ability to accurately approximate modeling errors informs statistics on model confidence and improves quantitative results from frameworks using numerical models in prediction, tomography, and signal processing. Further to this, the compensation of highly nonlinear and non-Gaussian modeling errors, arising in many ill-conditioned systems aiming to capture complex physics, is a historically difficult task. In this work, we address this challenge by proposing a neural network approach capable of accurately approximating and compensating for such modeling errors in augmented direct and inverse problems. The viability of the approach is demonstrated using simulated and experimental data arising from differing physical direct and inverse problems.
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