In dieser Arbeit wird das Potenzial von Machine-Learning-Algorithmen (ML) zur Verbesserung der Parametrisierung von großskaligen atmosphärischen Simulationen untersucht. Herkömmliche Ansätze verwenden oft Vereinfachungen oder rechenintensive Methoden. Diese Arbeit beabsichtigt, einen physikalisch konsistenten…
Two hybrid quantum-classical reservoir computing models are presented to reproduce the low-order statistical properties of a two-dimensional turbulent Rayleigh-Bénard convection flow at a Rayleigh number Ra=105 and Prandtl number Pr=10. These properties comprise the mean vertical profiles of the root…
The spatial prediction of the turbulent flow of the unsteady von Kármán vortex street behind a cylinder at Re = 1000 is studied. For this, an echo state network (ESN) with 6000 neurons was trained on the raw, low-spatial resolution data from particle image velocimetry. During prediction, the ESN is provided…
Abstract The prediction of turbulent flow by the application of machine learning (ML) algorithms to big data is a concept currently in its infancy which requires further development. It is of special importance if the aim is a prediction that is good in a statistical sense or if the vector fields should…