Reinforcement learning based control of dynamical systems
In this project we employ novel AI techniques from reinforcement learning, sequence modelling and dimensionality reduction to learn complex controllers efficiently.
Michiel Straat
Postdoctoral Research Group Leader “Lifelong Machine Learning for Physical Systems”
My research interests include Machine Learning for Physical Systems and the theory of Neural Networks.
Publications
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime
We build a robust reinforcement learning agent to control Rayleigh-Bénard Convection in turbulent conditions and compare against …
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-B’enard Convection (RBC), a model for convection processes …
Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection
We build a linear dynamical model for complex fluid flows using a Machine Learning architecture based on Koopman theory.
Talks
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
I present our publication on the use of Fourier Neural Operator models as surrogates for convection dynamics.
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
In this talk I present our work on harnessing zero-shot superresolution surrogate models FNO for convection.
Koopman-based Modeling of Rayleigh-Bénard Convection
In this talk I will present our recent work on Koopman-based surrogate modeling of Rayleigh-Bénard Convection.
