معرفی
Felix Schindler is a Researcher at the Institute for Analysis and Numerical Analysis, part of the Department of Mathematics and Computer Science at the University of Münster. His work bridges numerical analysis, machine learning, and scientific computing, with a focus on model reduction for partial differential equations (PDEs), adaptive algorithms, and computational efficiency.
Research Interests include:
- Numerical analysis of parametric and multiscale PDEs
- Localized reduced basis methods (LRBM) and adaptive enrichment
- Integration of model order reduction (MOR) with machine learning (ML)
- Conservative flux reconstruction techniques
- Development of software libraries like dune-xt and pyMOR
Recent Publications highlight trends in applying deep kernel models for surrogate modeling, localized training strategies for PDE-constrained optimization, and hybrid full/reduced-order pipelines for reactive flow prediction. His work emphasizes certified error control, hierarchical adaptivity, and cross-disciplinary computational frameworks.
Collaborations span institutions such as AIMS Senegal, Springer Nature, and DUNE project teams. He actively contributes to conferences like GAMM, ENUMATH, and Algoritmy.
