2 citations · 3 across the 6 of their papers we have counts for
6 papers
PnP-DA: Towards Principled Plug-and-Play Integration of Variational Data Assimilation and Generative Models
Yongquan Qu, Matthieu Blanke, Sara Shamekh +1
Earth system modeling presents a fundamental challenge in scientific computing: capturing complex, multiscale nonlinear dynamics in computationally efficient models while minimizin…
Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling
Matthieu Blanke, Yongquan Qu, Sara Shamekh +1
Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibilit…
Neural Incremental Data Assimilation
Matthieu Blanke, Ronan Fablet, Marc Lelarge
Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmos…
Interpretable Meta-Learning of Physical Systems
Matthieu Blanke, Marc Lelarge
Machine learning methods can be a valuable aid in the scientific process, but they need to face challenging settings where data come from inhomogeneous experimental conditions. Rec…
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
Matthieu Blanke, Marc Lelarge
Model-based reinforcement learning is a powerful tool, but collecting data to fit an accurate model of the system can be costly. Exploring an unknown environment in a sample-effici…
Online greedy identification of linear dynamical systems
Matthieu Blanke, Marc Lelarge
This work addresses the problem of exploration in an unknown environment. For linear dynamical systems, we use an experimental design framework and introduce an online greedy polic…