Smyl, D;
Tallman, TN;
Black, JA;
Hauptmann, A;
Liu, D;
(2021)
Learning and correcting non-Gaussian model errors.
Journal of Computational Physics
, 432
, Article 110152. 10.1016/j.jcp.2021.110152.
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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.
| Type: | Article |
|---|---|
| Title: | Learning and correcting non-Gaussian model errors |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.jcp.2021.110152 |
| Publisher version: | http://dx.doi.org/10.1016/j.jcp.2021.110152 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Finite element method, Inverse problems, Model errors, Neural networks, Non-linearity, Tomography |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10122536 |
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| 2. |  Russian Federation | 1 |
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