معرفی
Dr. Julia Schleuss is a Researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster. She is affiliated with the Mathematics Münster Graduate School and contributes to research in numerical analysis, machine learning, and scientific computing.
- PhD in Mathematics (2019–2023), University of Münster
- MSc in Mathematics with minor Economics (2016–2019), University of Münster
- BSc in Mathematics with minor Business Administration (2013–2016), University of Münster
Her research focuses on model order reduction, multiscale methods, and domain decomposition techniques for partial differential equations (PDEs). Recent work includes time-parallel approximation spaces and residual localization strategies, bridging numerical methods with machine learning for high-dimensional problems.
Selected publications address optimal local approximation spaces for parabolic PDEs (2022), randomized quasi-optimal time-domain decomposition (2023), and localized training/enrichment strategies (2024). These works emphasize computational efficiency and adaptability for complex systems.
She contributes to teaching as a collaborator in courses such as Numerical Methods for PDEs, Nonlinear Modeling in Natural Sciences, and Applied Functional Analysis, working with professors like Mario Ohlberger and Christian Engwer.
