Thorben Markmann
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View article: Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime Open
Data-driven flow control has significant potential for industry, energy systems, and climate science. In this work, we study the effectiveness of Reinforcement Learning (RL) for reducing convective heat transfer in the 2D Rayleigh-Bénard C…
View article: Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators Open
We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-Bénard Convection (RBC), a model for convection processes that occur in nature and industrial settings. We compare the prediction accuracy and model properties of FNO sur…
Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection Open
Several related works have introduced Koopman-based Machine Learning architectures as a surrogate model for dynamical systems. These architectures aim to learn non-linear measurements (also known as observables) of the system's state that …