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Title:

Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Grigo, Constantin; Koutsourelakis, P.-S.
Pages contribution:
19
Abstract:
We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the coefficients (e.g. material parameters) exhibit random, fine scale variability. The direct solution to such problems requires grids that are small enough to resolve this fine scale variability which unavoidably requires the repeated solution of very large systems of algebraic equations. We establish a physically inspired, data-driven coarse-grained model which learns a low- dimensional set of microstructural feature...     »
Keywords:
Reduced-order modeling, generative Bayesian model, SPDE, effective material properties
Book / Congress title:
UNCECOMP 17
Congress (additional information):
2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
Organization:
Eccomas Thematic Conferences
Date of congress:
15 - 17 June 2017
Year:
2017
Reviewed:
ja
Language:
en
Publication format:
WWW
Fulltext / DOI:
doi:10.7712/120217.5356.16731
WWW:
uncecomp17
TUM Institution:
Continuum Mechanics Group, Department of Mechanical Engineering
Format:
Text
 BibTeX