Dr. Emil Løvbak, Computational Science and Mathematical Methods Group at SCC, Karlsruhe Institute of Technology (KIT) Title: Monte Carlo Adjoints with Reversible Random Number Generators Abstract: Kinetic equations, modeling particles in a position-velocity phase space, have many high-impact application areas, … Continue reading →

Yan Muller, Department of Computer Science, University of Kaiserslautern-Landau (RPTU) Title: Partial Least Squares Regression Abstract: This thesis investigates the use of Partial Least Squares (PLS) regression for dimensionality reduction in machine learning applications. A simple linear experiment first demonstrates … Continue reading →

Julia Manger, Department of Computer Science, University of Kaiserslautern-Landau (RPTU) Title: Bayesian Optimization with Inequality Constraints Abstract: Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions, often applied in engineering and machine learning. Many real-world problems involve … Continue reading →

Dr. Zebang Shen, Lecturer at the Department of Computer Science, Institute for Machine Learning, ETH Zürich, Switzerland Title: Provable Maximum Entropy Manifold Exploration via Diffusion Models Abstract: Exploration is critical for solving real-world decision-making problems such as scientific discovery, where … Continue reading →

Dr. Roman Pöschl, Laboratoire de Physique des 2 Infinis Ir`ene Joliot-Curie (IJCLab) / Universit´e Paris-Saclay, 91405 Orsay, France Title: The Detector R&D (DRD) on Calorimetry – Introduction and overview Abstract: -> Download Slides   Calorimeters play a pivotal role in … Continue reading →

Alexander Schilling, Chair for Scientific Computing (SciComp), University of Kaiserslautern-Landau (RPTU) Title: Proton Therapy Range Verification with a Digital Tracking Calorimeter Abstract: Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged … Continue reading →

Raju Ram, Bosch, Leonberg, Germany Title: Hybrid Parallel Preconditioners in Domain Decomposition Methods: Achieving Robustness and Scalability in the Solution of Large-Scale Linear Systems Abstract: Krylov subspace solvers like GMRES and preconditioners such as ILU are widely used for solving … Continue reading →

Prof. Dr. Alexander Heinlein, Numerical Analysis Group, TU Delft, The Netherlands Title: Domain decomposition for neural networks Abstract: -> Download Slides   Scientific machine learning (SciML) is a rapidly evolving research field that combines techniques from scientific computing and machine … Continue reading →

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 … Continue reading →

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 … Continue reading →