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

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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

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

March 2021

Successful EXIST Start Up phinc GmbH from the University of Stuttgart

News
3/22/21

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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Contact