Michael Feischl

Michael Feischl is a Professor for Computational PDEs at the Institut für Analysis und Scientific Computing, TU Wien. His research focuses on numerical methods for partial differential equations, stochastic perturbations, machine learning mathematics, computational micromagnetism, and optimal mesh refinement techniques. He leads the ERC Consolidator Grant project "New Frontiers in Optimal Adaptivity" (2024–2029), which aims to develop mathematically guaranteed optimal adaptive algorithms for time-dependent PDEs.

Feischl's research interests include the numerical analysis of PDEs with stochastic components, computational micromagnetism (particularly the Landau-Lifshitz-Gilbert equation), and the intersection of machine learning with computational mathematics. His work emphasizes adaptive algorithms that maximize accuracy while minimizing computational costs.

His publications demonstrate a strong emphasis on adaptive finite element methods, neural network theory, and computational micromagnetism. Recent articles explore optimal time-stepping, neural operator networks, and convergence analyses for complex physical systems. Trends include machine learning-enhanced numerical methods and rigorous mathematical foundations for computational physics.

Awards:

• ERC Consolidator Grant: "New Frontiers in Optimal Adaptivity" (2024–2029)

Feischl has held prior positions as Associate Professor at TU Wien (2019–2022), Professor at the University of Bonn (2018–2019), and Junior Research Group Leader at KIT (2017–2018). He obtained his doctorate from TU Wien in 2015.