AdaptLocalMOR2013: Difference between revisions
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| '''Markus Hegland''' (Australian National University) | | '''Markus Hegland''' (Australian National University) | ||
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Computing density estimators with maximum a posteriori and sparse grids | [http://www5.in.tum.de/workshops/AdaptLocalMOR2013/slides/Hegland.pdf Computing density estimators with maximum a posteriori and sparse grids] | ||
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| '''Felix Albrecht''' (University of Münster) | | '''Felix Albrecht''' (University of Münster) | ||
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The localized reduced basis method | [http://www5.in.tum.de/workshops/AdaptLocalMOR2013/slides/Albrecht.pdf The localized reduced basis method] | ||
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| '''Bernhard Wieland''' (Ulm University) | | '''Bernhard Wieland''' (Ulm University) | ||
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Implicit Partitioning of Unknown Parameter Domains in the Context of the RBM | [http://www5.in.tum.de/workshops/AdaptLocalMOR2013/slides/Wieland.pdf Implicit Partitioning of Unknown Parameter Domains in the Context of the RBM] | ||
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| '''Lihong Feng''' (MPI Magdeburg) | | '''Lihong Feng''' (MPI Magdeburg) | ||
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An a posteriori error bound for linear parametric systems | [http://www5.in.tum.de/workshops/AdaptLocalMOR2013/slides/Feng.pdf An a posteriori error bound for linear parametric systems] | ||
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| '''Qifeng Liao''' (MIT) | | '''Qifeng Liao''' (MIT) | ||
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A Domain Decomposition Approach for Uncertainty Analysis | [http://www5.in.tum.de/workshops/AdaptLocalMOR2013/slides/Liao.pdf A Domain Decomposition Approach for Uncertainty Analysis] | ||
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Revision as of 16:13, 29 September 2013
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
Germany
Organizers: Hans-Joachim Bungartz (TUM), Karen Willcox (MIT), and Benjamin Peherstorfer (TUM)
Description
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).
Participation
Please register in advance here (closed).
Program
time | speaker/title |
---|---|
10:00 - 10:40 | Markus Hegland (Australian National University)
|
10:40 - 11:20 | Felix Albrecht (University of Münster)
|
11:20 - 12:00 | Bernard Haasdonk (University of Stuttgart)
|
12:00 - 01:00 | lunch break |
01:00 - 01:40 | Bernhard Wieland (Ulm University)
|
01:40 - 02:20 | Benjamin Stamm (UPMC - Paris VI)
|
02:20 - 02:40 | coffee break |
02:40 - 03:20 | Lihong Feng (MPI Magdeburg)
|
03:20 - 04:00 | Qifeng Liao (MIT)
|
Abstracts
You can download the book of abstracts.
How to get there
The website of the TUM Graduate School contains traveling informations here
Contact
Contact person is Benjamin Peherstorfer
Acknowledgement
The workshop is supported by the MIT-Germany Seed Fund and by the TUM-IAS focus group on high-performance computing.