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https://cwi-nl.zoom.us/j/88489988455?pwd=NUZwRXN1alU1ZGJTbVhJc2o3L000dz09
Meeting ID: 884 8998 8455
Passcode: 303356
Marius Kurz (Centrum Wiskunde & Informatica): Learning to Flow: Machine Learning and Exascale Computing for Next-Generation Fluid Dynamics
The computational sciences have become an essential driver for understanding the dynamics of complex, nonlinear systems ranging from the dynamics of the earth’s climate to obtaining information about a
patient’s characteristic blood flow to derive personalized approaches in medical therapy. These advances can be ascribed on one hand to the exponential increase in available computing power, which has allowed the simulation of increasingly large and complex problems and has led to the emerging generation of exascale systems in high-performance computing (HPC). On the other hand, methodological advances in discretization methods and the modeling of turbulent flow have increased the fidelity of simulations in fluid dynamics significantly. Here, the recent advances in machine learning (ML) have opened a whole field of novel, promising modeling approaches.
This talk will first introduce the potential of GPU-based simulation codes in terms of energy-to-solution using the novel GALÆXI code. Next, the integration of machine learning methods for large eddy simulation
will be discussed with emphasis on their a posteriori performance, the stability of the simulation, and the interaction between the turbulence model and the discretization. Based on this, Relexi is introduced as a
potent tool that allows employing HPC simulations as training environments for reinforcement learning models at scale and thus to converge HPC and ML.
