Numerical Mathematics

Welcome to the Homepage of the Research Group of Prof. Dr. B. Haasdonk:

We investigate and develop efficient and certified surrogate modelling techniques.

How to reach us

Directions

In particular we are working on

Model Reduction

Reduced basis methods for parametric problems.

Kernel Methods

Data-based modelling for high-dimensional function approximation, data-analysis, machine learning

Optimization, Control

Parameter optimization, feedback control, inverse Problems

Software-development

RBmatlab, KerMor, dune-rb, JaRMoS, CCMOR etc.

Applications

(most recent ones) Flow and transport-problems, porous media, heterogeneous domain decomposition problems, biomechanics, elastic multibody systems, soft-tissue-robotics, pervasive computing

Our Networks

Link SimTech

June 2024

Mathematisches Kolloquium

News
6/6/24

April 2023

"Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder" published in SISC

News
4/14/23

"Randomized Symplectic Model Order Reduction for Hamiltonian Systems"

News
4/14/23

"Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems"

News
4/14/23

"Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar"

News
4/13/22

Team

Members of our group

Research

Research projects, softwares in our group

Teaching

Lectures offered from our group

Publications

Publications of our group

Miscellaneous

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