Lionel Mathelin

发表

A continuous reinforcement learning strategy for closed-loop control in fluid dynamics

Bernd R. Noack, Laurent Cordier, B. R. Noack, 2017 .

Control-oriented model learning with a recurrent neural network

Alexandre Allauzen, Guillaume Wisniewski, Laurent Cordier, 2018 .

Shallow neural networks for fluid flow reconstruction with limited sensors

Steven L. Brunton, J. Nathan Kutz, Michael W. Mahoney, 2020, Proceedings of the Royal Society A.

Stochastic approaches to uncertainty quantification in CFD simulations

Thomas A. Zang, M. Yousuff Hussaini, Lionel Mathelin, 2005, Numerical Algorithms.

Control of a chaotic dynamical system with a Deep Reinforcement Learning approach

Alexandre Allauzen, Guillaume Wisniewski, Laurent Cordier, 2019 .

A statistical learning strategy for closed-loop control of fluid flows

M. Yousuff Hussaini, Lionel Mathelin, Florimond Guéniat, 2016, 1604.03392.

Supplementary material from "Control of chaotic systems by deep reinforcement learning"

Guillaume Wisniewski, L. Cordier, Lionel Mathelin, 2019 .

Observable Dictionary Learning for High-Dimensional Statistical Inference

Hisham Abou-Kandil, Lionel Mathelin, Kévin Kasper, 2017, Archives of Computational Methods in Engineering.

Control of chaotic systems by deep reinforcement learning

Alexandre Allauzen, Guillaume Wisniewski, Laurent Cordier, 2019, Proceedings of the Royal Society A.

Uncertainty Propagation for Turbulent, Compressible Flow in a Quasi-1D Nozzle Using Stochastic Methods

M. Yousuff Hussaini, Lionel Mathelin, Thomas A. Zang, 2003 .

Uncertainty Quantification in CFD Simulations: A Stochastic Spectral Approach

M. Yousuff Hussaini, Lionel Mathelin, 2003 .

Nonlinear optimal control using deep reinforcement learning

Alexandre Allauzen, Guillaume Wisniewski, Laurent Cordier, 2019 .

An adaptive compressed-sensing equation-free approach for closed-loop nonlinear control

Olivier Le Maitre, Lionel Mathelin, Luc Pastur, 2011 .

A parsimonious strategy for field estimation from wall-mounted sensors

Hisham Abou-Kandil, Lionel Mathelin, Kévin Kasper, 2015 .

A machine learning approach for constrained sensor placement

Hisham Abou-Kandil, Lionel Mathelin, Kevin Kasper, 2015, 2015 American Control Conference (ACC).

Diffusion Maps meet Nyström pdf

Steven L. Brunton, J. Nathan Kutz, N. Benjamin Erichson, 2018, ArXiv.

Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data pdf

Steven L. Brunton, Michael W. Mahoney, J. Nathan Kutz, 2019, ArXiv.

Closed-loop fluid flow control using a low dimensional model

Mohamed Abbas-Turki, Lionel Mathelin, Hisham Abou-Kandil, 2010, Math. Comput. Model..

Identification-Based Closed-Loop Control Strategies for a Cylinder Wake Flow

Ercan Atam, Lionel Mathelin, Laurent Cordier, 2017, IEEE Transactions on Control Systems Technology.

Leveraging the structure of dynamical systems for data-driven modeling pdf

Alexandre Allauzen, Lionel Mathelin, Onofrio Semeraro, 2021, ArXiv.

Dual-based error analysis for uncertainty quantification in a chemical system

Olivier Le Maitre, Lionel Mathelin, 2007 .

Quantification of uncertainty from high-dimensional scattered data via polynomial approximation

Lionel Mathelin, L. Mathelin, 2013, 1302.7083.

Uncertainty Propagation for a Turbulent, Compressible Nozzle Flow Using Stochastic Methods

T. A. Zang, Lionel Mathelin, Thomas A. Zang, 2004 .

A posteriori error analysis for stochastic finite element solutions of fluid flows with parametric uncertainties

Lionel Mathelin, O. P. Le Maitre, 2006 .

A compressed-sensing approach for closed-loop optimal control of nonlinear systems

Lionel Mathelin, Luc Pastur, Olivier Le Maître, 2012 .

Uncertainty quantification in a chemical system using error estimate-based mesh adaption

Lionel Mathelin, Olivier P. Le Maître, L. Mathelin, 2012 .

Dual-based {\itshape a posteriori} error estimate for stochastic finite element methods

Olivier Le Maitre, Lionel Mathelin, O. L. Maître, 2007 .