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Workshop on Adaptive and Local Model Order Reduction with Machine Learning for Parametrized Systems

September 19, 2013
10:00 AM - 4:00 PM
Room: FMI 02.13.010TUM GS/IGSSE lecture room (5530.EG.003)
Department of Informatics
Boltzmannstr. 3Boltzmannstr. 17
85748 Garching

Organizers: Hans-Joachim Bungartz (TUM), Karen Willcox (MIT), and Benjamin Peherstorfer (TUM)



Most of today's simulations in computational science and engineering are solved many times in a row for different parameter configurations, e.g., in optimization, uncertainty quantification, and statistical inverse problems. To cope with the consequentially increased computational costs, model order reduction methods approximate the large-scale simulations with low-cost surrogates by solving the problem not in a general, high-dimensional solution space but in a problem-dependent, low-dimensional subspace. Classical approaches construct one subspace and use it for all parameter configurations and time steps. In contrast, adaptive and local model reduction methods construct multiple low-dimensional subspaces, each of them tailored to a particular region of characteristic system behavior. Machine learning techniques are a versatile way to detect these characteristic system behaviors from data and to derive reduced-order models with the obtained information.

This workshop brings together scientists to discuss recent advances in adaptive and local model order reduction. It also wraps up an MIT-TUM research collaboration funded by the MIT Germany Seed Fund (MISTI) where adaptive and local methods have been investigated. The workshop is organized together with the TUM Institute for Advanced Study and the focus group on high-performance computing (HPC).


Please register in advance here (closed).


time speaker/title
10:00 - 10:40 Markus Hegland (Australian National University)
Computing density estimators with maximum a posteriori and sparse grids
10:40 - 11:20 Felix Albrecht (University of Münster)
The localized reduced basis method
11:20 - 12:00 Bernard Haasdonk (University of Stuttgart)
Offline and Online Adaptivity in Reduced Basis Methods
12:00 - 01:00 lunch break
01:00 - 01:40 Bernhard Wieland (Ulm University)
Implicit Partitioning of Unknown Parameter Domains in the Context of the RBM
01:40 - 02:20 Benjamin Stamm (UPMC - Paris VI)
Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces
02:20 - 02:40 coffee break
02:40 - 03:20 Lihong Feng (MPI Magdeburg)
An a posteriori error bound for linear parametric systems
03:20 - 04:00 Qifeng Liao (MIT)
A Domain Decomposition Approach for Uncertainty Analysis


You can download the book of abstracts.

How to get there

The website of the TUM Graduate School contains traveling informations here


Contact person is Benjamin Peherstorfer


The workshop is supported by the MIT-Germany Seed Fund and by the TUM-IAS focus group on high-performance computing.