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AGH projects

Here you can find the projects hosted within our group.

For more information, klick on the project titles or feel free to contact us via email or phone.

Sparse Gaussian Process Approximation and Application for Dynamical Data-Assimilation

Principal investigators: Bernard Haasdonk   Holger Ulmer  

Staff: Roman Föll  

Begin: April 1, 2016   End: April 1, 2019


Sparse Gaussian Process Approximation and Application for Dynamical Data-Assimilation

Certified Model Order Reduction for Coupled Mechanical Systems

Principal investigators: Bernard Haasdonk   Jörg Fehr  

Staff: Ashish Bhatt  

Begin: February 1, 2017   End: January 31, 2019


This project aims at certified model order reduction of coupled mechanical systems. Coupled mechanical systems are generally high dimensional nonlinear models and therefore their repeated numerical simulation with varying parameters is prohibitively expansive. One therefore employs model order reduction (MOR) techniques to reduce the order of the mechanical system in order to reduce time complexity of numerical simulations. The process of model order reduction inevitably introduces additional simulation error and estimating this error is important in many applications.

Greedy kernel approximation

Principal investigators: Bernard Haasdonk  

Staff: Gabriele Santin  

Begin: November 1, 2015   End: September 30, 2019


This project deals with greedy algorithms for kernel-based approximation. The goal is to use data samples to construct sparse surrogate models of possibly high dimensional functions.

Feedback Control of Parametric PDEs with RB-Surrogate Models

Principal investigators: Prof. Dr. Bernard Haasdonk  

Staff: Andreas Schmidt  

Begin: April 1, 2014   End: November 1, 2017


In this project, we apply Reduced Basis Methods to optimal feedback control problems for parametrized control problems, governed by partial differential equations.

Reduced Basis Methods for Heterogeneous Domain Decomposition Problems

Principal investigators: Bernard Haasdonk  

Staff: Immanuel Martini  

Begin: August 1, 2012   End: March 31, 2017


This project aims at reduced basis (RB) approximation of heterogeneous domain decomposition problems. It comprises development of suitable methods, error analysis and efficient implementation in sophisticated software environments.

MoRePaS 09: International Workshop on Model Reduction of Parametrized Systems

Principal investigators: B. Haasdonk   M. Ohlberger   T. Tonn   K. Urban  

Begin: January 1, 1970   End: January 1, 1970


Indefinite Kernel Methods and Learning in General Proximity Spaces

Principal investigators: B. Haasdonk   E. Pekalska  

Begin: January 1, 1970   End: January 1, 1970


Modellreduktion zur Simulation von Transportprozessen und Anwendungen in Brennstoffzellen

Principal investigators: B. Haasdonk  

Staff:  

Begin: January 1, 1970   End: January 1, 1970


Reduzierte Basis Methoden zur Modellreduktion für Nichtlineare Parametrisierte Evolutionsgleichungen

Principal investigators: B. Haasdonk   M. Ohlberger  

Staff: M. Drohmann  

Begin: January 1, 2009   End: December 31, 2010


In the course of this project, reduced basis methods (RB) for parametrized nonlinear transport problems shall be developed. RB methods are a model reduction technique providing efficient, reduced models which allow fast parameter variations through an offline/online decomposition.

Reduced Basis Methods for Higher Order Evolution Problems and Application in Optimization

Principal investigators: Bernard Haasdonk  

Staff: Markus Dihlmann  

Begin: November 15, 2009   End: November 14, 2012


In the current project, we will extend the RB-methodology to a general class of higher-order evolution systems. Furthermore we will investigate more goal oriented basis-generation techniques to improve the RB-methodology.

Multiscale Reduced Basis Methods

Principal investigators: Bernard Haasdonk  

Staff: Sven Kaulmann  

Begin: May 2, 2011   End: January 31, 2012


Maschinelles Lernen zur Simulationsbasierten Modellapproximation

Principal investigators: Bernard Haasdonk  

Staff: Daniel Wirtz  

Begin: January 1, 1970   End: January 1, 1970


Dieses Projekt befasst sich mit der Modellreduktion von parametrisierten, nichtlinearen dynamischen Systemen und kombiniert Techniken aus der klassischen Modellreduktion mit Methoden des Maschinellen Lernens.

KerMor - Kernel Methods for Model Order Reduction

Principal investigators: Bernard Haasdonk  

Staff: Daniel Wirtz  

Begin: February 1, 2010   End: October 31, 2010


Our project is concerned with tackling the difficulties when handling large scale, parametrized nonlinear dynamical systems that occur naturally in biochemical settings as cell apoptosis simulation, for example.

 Have a nice day!