TY  - JOUR
A1  - Smyl, D
A1  - Tallman, TN
A1  - Black, JA
A1  - Hauptmann, A
A1  - Liu, D
KW  - Finite element method
KW  -  Inverse problems
KW  -  Model errors
KW  -  Neural networks
KW  -  Non-linearity
KW  -  Tomography
ID  - discovery10122536
VL  - 432
AV  - public
TI  - Learning and correcting non-Gaussian model errors
JF  - Journal of Computational Physics
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
UR  - http://dx.doi.org/10.1016/j.jcp.2021.110152
Y1  - 2021/05/01/
N2  - 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.
ER  -