Curriculum learning for data-driven modeling of dynamical systems.

The reliable prediction of the temporal behavior of complex systems is key in numerous scientific fields. This strong interest is however hindered by modeling issues: Often, the governing equations describing the physics of the system under consideration are not accessible or, when known, their solution might require a computational time incompatible with the prediction time constraints. Not surprisingly, approximating complex systems in a generic functional format and informing it ex-nihilo from available observations has become common practice in the age of machine learning, as illustrated by the numerous successful examples based on deep neural networks.

Shallow neural networks for fluid flow reconstruction with limited sensors.

In many applications, it is important to reconstruct a fluid flow field, or some other high-dimensional state, from limited measurements and limited data. In this work, we propose a shallow neural network-based learning methodology for such fluid flow reconstruction. Our approach learns an end-to-end mapping between the sensor measurements and the high-dimensional fluid flow field, without any heavy preprocessing on the raw data.

Control of chaotic systems by deep reinforcement learning.

Deep reinforcement learning (DRL) is applied to control a nonlinear, chaotic system governed by the one-dimensional Kuramoto-Sivashinsky (KS) equation. DRL uses reinforcement learning principles for the determination of optimal control solutions and deep neural networks for approximating the value function and the control policy. Recent applications have shown that DRL may achieve superhuman performance in complex cognitive tasks.

Comparison between two models of cooling surfaces using blowing.

To protect surfaces against high temperatures, the blowing through a porous material is studied. The geometry is that of a circular cylinder in cross-flow and the effectiveness of the blowing for the thermal protection is numerically investigated. Two models are developed for the blowing simulation and comparisons are made with experimental data obtained in a heated wind-tunnel.