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Bayesian model and dimension reduction for uncertainty propagation:
  applications in random media
v1v2 (latest)

Bayesian model and dimension reduction for uncertainty propagation: applications in random media

7 November 2017
Constantin Grigo
P. Koutsourelakis
ArXiv (abs)PDFHTML

Papers citing "Bayesian model and dimension reduction for uncertainty propagation: applications in random media"

9 / 9 papers shown
Variational Bayesian surrogate modelling with application to robust
  design optimisation
Variational Bayesian surrogate modelling with application to robust design optimisation
Thomas A. Archbold
Ieva Kazlauskaite
F. Cirak
303
3
0
23 Apr 2024
Semi-supervised Invertible Neural Operators for Bayesian Inverse
  Problems
Semi-supervised Invertible Neural Operators for Bayesian Inverse ProblemsComputational Mechanics (Comput. Mech.), 2022
Sebastian Kaltenbach
P. Perdikaris
P. Koutsourelakis
377
37
0
06 Sep 2022
Self-supervised optimization of random material microstructures in the
  small-data regime
Self-supervised optimization of random material microstructures in the small-data regimenpj Computational Materials (npj Comput Mater), 2021
Maximilian Rixner
P. Koutsourelakis
AI4CE
135
18
0
05 Aug 2021
A probabilistic generative model for semi-supervised training of
  coarse-grained surrogates and enforcing physical constraints through virtual
  observables
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observablesJournal of Computational Physics (JCP), 2020
Maximilian Rixner
P. Koutsourelakis
AI4CE
293
34
0
02 Jun 2020
Incorporating physical constraints in a deep probabilistic machine
  learning framework for coarse-graining dynamical systems
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systemsJournal of Computational Physics (JCP), 2019
Sebastian Kaltenbach
P. Koutsourelakis
AI4CE
558
39
0
30 Dec 2019
A physics-aware, probabilistic machine learning framework for
  coarse-graining high-dimensional systems in the Small Data regime
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regimeJournal of Computational Physics (JCP), 2019
Constantin Grigo
P. Koutsourelakis
AI4CE
360
31
0
11 Feb 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate
  Modeling and Uncertainty Quantification without Labeled Data
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Yinhao Zhu
N. Zabaras
P. Koutsourelakis
P. Perdikaris
PINNAI4CE
520
1,007
0
18 Jan 2019
A data-driven model order reduction approach for Stokes flow through
  random porous media
A data-driven model order reduction approach for Stokes flow through random porous media
Constantin Grigo
P. Koutsourelakis
DiffMAI4CE
43
0
0
21 Jun 2018
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate
  Modeling and Uncertainty Quantification
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
Yinhao Zhu
N. Zabaras
UQCVBDL
458
718
0
21 Jan 2018
1
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