Thorsten Raasch

Thorsten Raasch is a Professor of Numerical Mathematics at the Institute of Mathematics, Johannes Gutenberg University Mainz, Germany. He is affiliated with the Department of Physics, Mathematics, and Computer Science, and leads research in numerical analysis, particularly adaptive methods for PDEs and inverse problems.

Research Interests: His work focuses on adaptive discretization methods for partial differential equations and inverse problems, non-smooth numerical optimization, wavelet systems, multilevel frames, and GPGPU computing. These interests are deeply rooted in applied and computational mathematics, with applications in scientific computing and engineering.

The most recent articles show a strong trend in developing adaptive wavelet-based numerical schemes for solving inverse and stochastic PDEs, with a particular emphasis on sparsity, convergence analysis, and preconditioning. His publications span high-impact journals in numerical analysis and inverse problems, indicating sustained scholarly activity.

Teaching: He has taught a variety of courses including Fundamentals of Numerics, Convex Optimization, Numerical PDEs, Spectral Methods, and advanced seminars on topics like semi-smooth Newton methods and reduced basis methods.

Thorsten Raasch earned his doctorate in 2007 from Philipps University of Marburg and has held academic positions at JGU Mainz, the University of Siegen, and the University of Rostock. He was appointed W2 Professor at JGU Mainz in 2015 and previously held a junior professorship there. No scientific awards are mentioned in the provided texts.

Advising and Grants: While no students or grants are explicitly listed, his role as a professor and active researcher suggests involvement in supervising graduate students and participating in research projects, possibly within the 'Mathematics of Computation' research network.

Labs and Teams: He is part of the Numerical Mathematics Working Group at JGU Mainz and contributes to the 'Mathematics of Computation' research network, indicating collaboration within a structured research environment.