research
∙
03/18/2023
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensive...
share
research
∙
10/25/2022
Online model error correction with neural networks in the incremental 4D-Var framework
Recent studies have demonstrated that it is possible to combine machine ...
research
∙
03/21/2022
Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential
This study proposes voltage-dependent-synaptic plasticity (VDSP), a nove...
research
∙
12/14/2021
A Tutorial on Bayesian Data Assimilation
This tutorial provides a broad introduction to Bayesian data assimilatio...
research
∙
07/23/2021
State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics
In a recent methodological paper, we have shown how to learn chaotic dyn...
research
∙
07/23/2021
A comparison of combined data assimilation and machine learning methods for offline and online model error correction
Recent studies have shown that it is possible to combine machine learnin...
research
∙
10/23/2020
Using machine learning to correct model error in data assimilation and forecast applications
The idea of using machine learning (ML) methods to reconstruct the dynam...
research
∙
09/09/2020
Combining data assimilation and machine learning to infer unresolved scale parametrisation
In recent years, machine learning (ML) has been proposed to devise data-...
research
∙
06/06/2020
Online learning of both state and dynamics using ensemble Kalman filters
The reconstruction of the dynamics of an observed physical system as a s...
research
∙
01/17/2020
Bayesian inference of dynamics from partial and noisy observations using data assimilation and machine learning
The reconstruction from observations of high-dimensional chaotic dynamic...
research
∙
01/06/2020
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
A novel method, based on the combination of data assimilation and machin...
research
∙
08/12/2019
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
One of the most exciting applications of Spin Torque Magnetoresistive Ra...
research
∙
07/30/2018
Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review
This paper is a review of a crucial topic in data assimilation: the join...
research
∙
01/25/2018
Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures
This paper studies inflation: the complementary scaling of the state cov...
research
∙
10/27/2017
An iterative ensemble Kalman filter in presence of additive model error
The iterative ensemble Kalman filter (IEnKF) in a deterministic framewor...
research
∙
09/19/2017
