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Titel:

Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Grigo, Constantin; Koutsourelakis, P.-S.
Seitenangaben Beitrag:
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...     »
Stichworte:
Reduced-order modeling, generative Bayesian model, SPDE, effective material properties
Kongress- / Buchtitel:
UNCECOMP 17
Kongress / Zusatzinformationen:
2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering
Ausrichter der Konferenz:
Eccomas Thematic Conferences
Datum der Konferenz:
15 - 17 June 2017
Jahr:
2017
Reviewed:
ja
Sprache:
en
Erscheinungsform:
WWW
Volltext / DOI:
doi:10.7712/120217.5356.16731
WWW:
uncecomp17
TUM Einrichtung:
Continuum Mechanics Group, Department of Mechanical Engineering
Format:
Text
 BibTeX