We build a robust reinforcement learning agent to control Rayleigh-Bénard Convection in turbulent conditions and compare against conventional control methods.

We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-B’enard Convection (RBC), a model for convection processes that occur in nature and industrial settings and that yields zero-shot super resolution. We compare the prediction accuracy and model properties of FNO surrogates to two popular surrogates used in fluid dynamics.

We build a linear dynamical model for complex fluid flows using a Machine Learning architecture based on Koopman theory.

In this work we show theoretically that using GELU activation in neural networks induces continuous phase transitions and we analyse the location of the phase transition. We furthermore introduce and analyse a combination of Erf and GELU activation.

We present a typical Industry 4.0 case study: the real-time quality estimation of steel in a high-throughput production line using Machine Learning methods.

A statistical physics based modelling framework is developed in which we study standard training algorithms (SGD, LVQ1) under concept drift and in addition compare ReLU vs sigmoidal activation.

Our systematic comparison of networks with ReLU and sigmoidal units in model situations reveals surprising differences in their training and generalization behavior.