Cheng, S;
Chen, J;
Anastasiou, C;
Angeli, P;
Matar, OK;
Guo, YK;
Pain, CC;
(2023)
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models.
Journal of Scientific Computing
, 94
(1)
, Article 11. 10.1007/s10915-022-02059-4.
Preview |
Text
Angeli_Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models_VoR.pdf Download (3MB) | Preview |
Abstract
Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines reduced-order surrogate models with a novel data assimilation (DA) technique used to incorporate real-time observations from different physical spaces. We make use of local smooth surrogate functions which link the space of encoded system variables and the one of current observations to perform variational DA with a low computational cost. The new system, named generalised latent assimilation can benefit both the efficiency provided by the reduced-order modelling and the accuracy of data assimilation. A theoretical analysis of the difference between surrogate and original assimilation cost function is also provided in this paper where an upper bound, depending on the size of the local training set, is given. The new approach is tested on a high-dimensional (CFD) application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle. Numerical results demonstrate that the proposed assimilation approach can significantly improve the reconstruction and prediction accuracy of the deep learning surrogate model which is nearly 1000 times faster than the CFD simulation.
| Type: | Article |
|---|---|
| Title: | Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10915-022-02059-4 |
| Publisher version: | https://doi.org/10.1007/s10915-022-02059-4 |
| Language: | English |
| Additional information: | © 2022 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Deep learning, Data assimilation, Reduced-order-modelling, Explainable AI, Recurrent neural networks |
| 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 Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10161754 |
Archive Staff Only
 |
View Item |
