Prof. Dr. Nicolas Gauger, Chair for Scientific Computing (SciComp), TU Kaiserslautern

SciComp Seminar Series

Please contact Prof. Gauger, if you want to register for an online talk in our SciComp Seminar Series or just to register for the seminar.

A list of the already scheduled talks can be found –> here:

Guillermo Suárez, Chair for Scientific Computing (SciComp), University of Kaiserslautern-Landau (RPTU)

Title: Reinforcement Learning Discovers Efficient Strategies for Active Flow Control

Abstract:

We explore the use of reinforcement learning to develop effective strategies for active flow control in unsteady fluid dynamics. In a two-dimensional computational fluid dynamics simulation of flow past a circular cylinder at a Reynolds number of 100, a reinforcement learning agent learns to manipulate dual side jets to alter the vortex shedding dynamics. Without any prior knowledge of the flow physics, the agent discovers a control policy that suppresses vortex-induced oscillations and achieves a drag reduction of nearly 10%. This performance is attained with minimal actuation effort, using jet mass flow rates of less than 0.5% of the incoming flow.

Building on these results, ongoing work investigates the integration of model-based reinforcement learning, aiming to reduce training time and improve generalization.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

Prof. Dr. Alexander Heinlein, Numerical Analysis Group, TU Delft, The Netherlands

Title: Domain decomposition for neural networks

Abstract:

Scientific machine learning (SciML) is a rapidly evolving research field that combines techniques from scientific computing and machine learning. This talk focuses on the application of domain decomposition methods to design neural network architectures and enhance neural network training. In particular, it first explores how domain decomposition techniques can be employed in neural network-based discretizations that can address forward and inverse problems involving partial differential equations, using physics-informed neural networks (PINNs) as well as neural operators. It further discusses domain decomposition-based neural networks and preconditioning strategies for randomized neural networks, where the resulting optimization problem becomes linear in both data-driven settings and PINNs involving linear differential operators. Finally, the talk explores the use of domain decomposition methods for traditional machine learning tasks, such as semantic image segmentation with convolutional neural networks (CNNs). Computational results show that domain decomposition methods can improve efficiency—both in terms of time and memory—as well as enhance accuracy and robustness.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

Raju Ram, Bosch, Leonberg, Germany

Title: tba

Abstract:

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How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

Alexander Schilling, Chair for Scientific Computing (SciComp), University of Kaiserslautern-Landau (RPTU)

Title: tba

Abstract:

tba

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

Dr. Roman Pöschl, Laboratoire de Physique des 2 Infinis Ir`ene Joliot-Curie (IJCLab) / Universit´e Paris-Saclay, 91405 Orsay, France

Title: tba

Abstract:

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How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1