3 papers
cs.LG2025
Ensemble Kalman filter in latent space using a variational autoencoder pair
Ivo Pasmans, Yumeng Chen, Tobias Sebastian Finn +2
Popular (ensemble) Kalman filter data assimilation (DA) approaches assume that the errors in both the a priori estimate of the state and those in the observations are Gaussian. For…
cs.LG2025
Machine learning for modelling unstructured grid data in computational physics: a review
Sibo Cheng, Marc Bocquet, Weiping Ding +20
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for co…
nlin.CD2024
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
Marc Bocquet, Alban Farchi, Tobias S. Finn +5
We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, fr…