Matthieu Blanke

Interpretable Meta-Learning of Physical Systems

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. Recent...

Neural Incremental Data Assimilation

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 atmosphere, from...

FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems

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-efficient manner is...

Online greedy identification of linear dynamical systems

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 policy where the...

Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling

Interpretable Meta-Learning of Physical Systems

FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems

Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling

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 plausibility of the...

PnP-DA: Towards Principled Plug-and-Play Integration of Variational Data Assimilation and Generative Models

Earth system modeling presents a fundamental challenge in scientific computing: capturing complex, multiscale nonlinear dynamics in computationally efficient models while minimizing forecast...