Michael Feischl

Michael Feischl is a Univ. Prof. at TU Wien's Institute for Analysis and Scientific Computing (E101). He specializes in numerical methods for partial differential equations, computational micromagnetism, and optimal adaptivity. Feischl leads the ERC Consolidator Project 'New Frontiers in Optimal Adaptivity' and has held academic positions at TU Wien, University of Bonn, and Karlsruhe Institute of Technology. His research interests span stochastic perturbations, finite element methods, and machine learning applications in computational mathematics.

Feischl's work includes groundbreaking contributions to adaptive finite element methods, optimal mesh refinement strategies, and the numerical analysis of the Landau-Lifshitz-Gilbert equation in micromagnetics. His recent publications focus on advancing computational techniques for PDEs, neural operator networks, and stochastic collocation methods. He has developed algorithms with guaranteed convergence properties and optimal complexity, contributing to both theoretical and applied aspects of computational science.

• Education: Dipl.-Ing. Dr.techn. (PhD in Technical Mathematics) from TU Wien
• Awards: ERC Consolidator Grant 2022
• Labs/Teams: Heads the 'Computational PDEs' research group at TU Wien's Institute for Analysis and Scientific Computing