Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

no code implementations • 18 Mar 2023 • Sibo Cheng, Cesar Quilodran-Casas, Said Ouala, Alban Farchi, Che Liu, Pierre Tandeo, Ronan Fablet, Didier Lucor, Bertrand Iooss, Julien Brajard, Dunhui Xiao, Tijana Janjic, Weiping Ding, Yike Guo, Alberto Carrassi, Marc Bocquet, Rossella Arcucci

Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.

Uncertainty Quantification

Combining data assimilation and machine learning to infer unresolved scale parametrisation

no code implementations • 9 Sep 2020 • Julien Brajard, Alberto Carrassi, Marc Bocquet, Laurent Bertino

Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.

BIG-bench Machine Learning

Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

no code implementations • 17 Jan 2020 • Marc Bocquet, Julien Brajard, Alberto Carrassi, Laurent Bertino

The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics.

Bayesian Inference BIG-bench Machine Learning +2