Research Projects
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Certified Model Order Reduction for Coupled Mechanical Systems
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Certified Model Order Reduction for Coupled Multi-Physics Systems
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Feedback Control of Parametric PDEs with RB-Surrogate Models
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Greedy Deep Kernel Methods for Data-based Modelling in Biomechanics
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Greedy kernel approximation
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Soft Tissue Robotics: Efficient Model reduction techniques for soft tissue simulations
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Sparse Gaussian Process Approximation and Application for Dynamical Data-Assimilation
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Uncertainty Quantification by Physics- and Data-Based Models for Mechanical Systems
Research
Former Research Projects
- Reduced Basis Methods for Heterogeneous Domain Decomposition Problems
- Reduced Basis Methods for Higher Order Evolution Problems and Application in Optimization
- Multiscale Reduced Basis Methods
- Machine learning for simulation-based model approximation
- KerMor - Kernel Methods for Model Order Reduction
Software
RBmatlab is a MATLAB library containing all our reduced simulation approaches for linear and nonlinear, affine or arbitrarily parameter dependent evolution problems with finite element, finite volume or local discontinuous Galerkin discretizations.
VKOGA is a MATLAB implementation of the Vectorial Kernel Orthogonal Greedy Algorithm. It includes the solution of interpolation problems and regularized approximation problems, and automatic parameter tuning.
KerMor is a MATLAB© library providing routines for model order reduction of dynamical systems using subspace projection and kernels methods.
JaRMoS stands for "Java Reduced Model Simulations" and aims to enable import and simulation of various reduced models from multiple sources on any java-capable platform.
KerMet-Tools toolbox provides routines for pattern analysis with kernel methods.
Contact
Bernard Haasdonk
Prof. Dr.Head of Group Numerical Mathematics
Dean of Studies (B.Sc./M.Sc. Mathematik)