Mathematik und Informatik

Prof. Dr. Christian Engwer, Angewandte Mathematik Münster: Institut für Analysis und Numerik

Investigator in Mathematics Münster
Field of expertise: Numerical analysis, machine learning and scientific computing

Private Homepagehttps://www.uni-muenster.de/AMM/engwer/team/engwer.shtml
Research InterestsNumerical methods for partial differential equations
Scoientific Computing
High-Performance Computing
Design and development of numerical software
Cut-cell methods
Numerical methods for surface PDEs and geometric PDEs
Biomedical Applications
Topics in
Mathematics Münster


T9: Multi-scale processes and effective behaviour
T10: Deep learning and surrogate methods
Current PublicationsEngwer, Christian; Ohlberger, Mario; Rebelt, Lukas Sectional Kolmogorov N-widths for parameter-dependent function spaces: A general framework with application to parametrized Friedrichs' systems. , 2025 online
Engwer, Christian; Ohlberger, Mario; Rebelt, Lukas Sectional Kolmogorov N-widths for parameter-dependent function spaces: A general framework with application to parametrized Friedrichs' systems. , 2025 online
Engwer, Christian; Ohlberger, Mario; Renelt, Lukas Model order reduction of an ultraweak and optimally stable variational formulation for parametrized reactive transport problems. SIAM Journal on Scientific Computing Vol. 46 (5), 2024 online
Engwer,Christian; Ohlberger, Mario; Renelt, Lukas Construction of local reduced spaces for Friedrichs' systems via randomized training. , 2024 online
Medani, Takfarinas; Garcia-Prieto, Juan; Tadel, Francois; Antonakakis, Marios; Erdbrügger, Tim; Höltershinken, Malte; Mead, Wayne; Schrader, Sophie; Joshi, Anand; Engwer, Christian; Wolters, Carsten H.; Mosher, John C.; Leahy, Richard M. Brainstorm-DUNEuro: An integrated and user-friendly Finite Element Method for modeling electromagnetic brain activity. NeuroImage Vol. 267, 2023 online
Renelt, Lukas; Ohlberger, Mario; Engwer, Christian An optimally stable approximation of reactive transport using discrete test and infinite trial spaces. Finite Volumes for Complex Applications X—Volume 2, Hyperbolic and Related ProblemsSpringer Proceedings in Mathematics & Statistics Vol. 2, 2023 online
Erdbrügger, T.; Westhoff, A.; Höltershinken, M.; Radecke, J.-O.; Buschermöhle,Y.; Buyx, A.; Wallois, F.; Pursiainen, S.; Gross, J.; Lencer, R.; Engwer, C.; Wolters, C.H. CutFEM forward modeling for EEG source analysis. Frontiers in Human Neuroscience Vol. 17, 2023 online
Renelt, Lukas; Ohlberger, Mario; Engwer, Christian An optimally stable approximation of reactive transport using discrete test and infinite trial spaces. Finite Volumes for Complex Applications X—Volume 2, Hyperbolic and Related ProblemsSpringer Proceedings in Mathematics & Statistics Vol. 2, 2023 online
Bastian P, Blatt M, Dedner A, Dreier N, Engwer C, Fritze R, Gräser C, Kempf D, Klöfkorn R, Ohlberger M, Sander O The DUNE Framework: Basic Concepts and Recent Developments. Computers & Mathematics with Applications Vol. 81, 2021, pp 75-112 online
Current ProjectsEXC 2044 - T09: Multiscale processes and effective behaviour Many processes in physics, engineering and life sciences involve multiple spatial and temporal scales, where the underlying geometry and dynamics on the smaller scales typically influence the emerging structures on the coarser ones. A unifying theme running through this research topic is to identify the relevant spatial and temporal scales governing the processes under examination. This is achieved, e.g., by establishing sharp scaling laws, by rigorously deriving effective scale-free theories and by developing novel approximation algorithms which balance various parameters arising in multiscale methods. online
EXC 2044 - T10: Deep learning and surrogate methods In this topic we will advance the fundamental mathematical understanding of artificial neural networks, e.g., through the design and rigorous analysis of stochastic gradient descent methods for their training. Combining data-driven machine learning approaches with model order reduction methods, we will develop fully certified multi-fidelity modelling frameworks for parameterised PDEs, design and study higher-order deep learning-based approximation schemes for parametric SPDEs and construct cost-optimal multi-fidelity surrogate methods for PDE-constrained optimisation and inverse problems. online
BlockXT – Block methods to transparently accelerate and vectorise time dependent simulations

Numerical methods for solving partial differential equations (PDE) are of central importance in many fields of application. Constantly increasing model details require almost unlimited computer power. To use modern hardware efficiently new dedicated numerical methods are necessary. Special challenges are posed by the increasing parallelism, especially the instruction level parallelism, as well as the ever increasing gap between memory and computing speed. The aim of this proposal is improve the hardware efficiency of a broad class of numerical methods for instationary PDEs. By developing modern numerical methods under the constraints posed by current hardware, we will provide approaches which are easily applicable to existing simulation codes. The numerical approach is based on preliminary work on block-Krylov methods. Combining them with parallel-in-time methods facilitates the transparent use of vector units of modern CPUs and by this allows to utilize a significant portion of the attainable peak performance. The approach allows for a speedup that would otherwise not be possible for classical low-order methods and does furthermore increase the local problem size, contributing to an improved strong-scaling. We will contribute to an improved hardware efficiency by development of new numerical methods, analysis of their performance, a hardware-efficient implementation and its validation.

online
BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging Software grant for integrating DUNEuro (http://www.duneuro.org) into Brainstorm (http://neuroimage.usc.edu/brainstorm) online
E-Mailchristian.engwer@uni-muenster.de
Phone+49 251 83-35067
FAX+49 251 83-32729
Room120.020
Secretary   Sekretariat Wernke
Frau Silvia Wernke
Telefon +49 251 83-35052
Fax +49 251 83-32729
Zimmer 120.001
AddressProf. Dr. Christian Engwer
Angewandte Mathematik Münster: Institut für Analysis und Numerik
Fachbereich Mathematik und Informatik der Universität Münster
Orléans-Ring 10
48149 Münster
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