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:

Prof. Dr. Bernat Font, Data-Informed Computational Fluid Dynamics (DI-CFD) research group, TU Delft, The Netherlands

Title: Data-informed CFD: From RL for active flow control to optimal numerical methods using AD

Abstract:

The advancement of scientific machine learning (SciML) has reshaped how we control, model, and compute turbulent flows. Together with the widespread use of graphics processing units, we are now in a unique position to perform scale-resolving simulations combining data-driven techniques with classic methods, yielding the data-informed CFD paradigm. In this talk, we will review established numerical methods and physical models which can be enhanced through data, including linear solvers, turbulence models, and active flow control techniques. We will also discuss the solver-in-the-loop optimization approach using automatic differentiation, thus avoiding well-known a-priori/a-posteriori errors. Last, we will conclude with basic guidelines in the use of SciML for CFD.

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

Maoni Ngowa Msinda, Chair for Scientific Computing (SciComp), RPTU University Kaiserslautern-Landau

Title: Modeling Glioblastoma Plasticity Using Partial Differential Equations and Machine Learning

Abstract:

The integration of mathematical modeling and machine learning is opening new directions in computational oncology. In this work, we present a partial integro-differential equation framework for modeling glioblastoma plasticity, incorporating anisotropic diffusion, convection, nonlocal interactions, and nonlinear reaction dynamics. Numerical simulations are performed using finite-difference discretizations together with fourth-order Runge–Kutta time integration, while parameter estimation is formulated as a PDE-constrained optimization problem. To efficiently compute gradients for large-scale inverse problems, we employ reverse-mode automatic differentiation using the CoDiPack library combined with a two-level uniform checkpointing strategy. Gradient-based optimization methods, including gradient descent and L-BFGS-B, are compared for recovering model parameters from observed tumor data. Finally, we discuss future directions involving spatially varying parameters, clinical data assimilation, and machine learning approaches for accelerated inverse modeling.

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