Learning and correcting non-Gaussian model errors

Danny Smyl

doi:10.1016/j.jcp.2021.110152

Learning and correcting non-Gaussian model errors

2021-05-01

11

OA

AI

DOI：10.1016/j.jcp.2021.110152

Original

Save

View PDF

Abstract

En 中文

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. (C) 2021 Elsevier Inc. All rights reserved.

Keywords:

Finite element method

Inverse problems

Model errors

Neural networks

Non-linearity

Tomography

Learning and correcting non-Gaussian model errors

Abstract

AI Summary

Journal

Organization