Uncertainty propagation using infinite mixture of gaussian processes and variational Bayesian inference
From MaRDI portal
Publication:729001
DOI10.1016/j.jcp.2014.12.028zbMath1351.76277OpenAlexW2021125631MaRDI QIDQ729001
Peng Chen, Ilias Bilionis, Nicholas Zabaras
Publication date: 20 December 2016
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2014.12.028
reservoir simulationDirichlet processuncertainty quantificationvariational inferenceinfinite mixture of Gaussian processesmulti-output Gaussian processnonparametric variational inference
Related Items
A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems, A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems, Predictive coarse-graining, On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models, Reduced-space Gaussian process regression for data-driven probabilistic forecast of chaotic dynamical systems, On the influence of over-parameterization in manifold based surrogates and deep neural operators, The stochastic quasi-chemical model for bacterial growth: variational Bayesian parameter update, Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks, Systems of Gaussian process models for directed chains of solvers, Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes, Generalized Multiscale Inversion for Heterogeneous Problems, Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification, Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation, Sparse polynomial chaos expansions using variational relevance vector machines
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A probabilistic graphical model approach to stochastic multiscale partial differential equations
- A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media
- Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification
- Multi-output local Gaussian process regression: applications to uncertainty quantification
- A stochastic mixed finite element heterogeneous multiscale method for flow in porous media
- Overlapping mixtures of Gaussian processes for the data association problem
- The local Dirichlet process
- Richardson-extrapolated sequential splitting and its application
- An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations
- The heterogeneous multiscale finite element method for elliptic homogenization problems in perforated domains
- Bayesian emulation of complex multi-output and dynamic computer models
- PRMLT
- A multiscale finite element method for elliptic problems in composite materials and porous media
- Rapid quantification of uncertainty in permeability and porosity of oil reservoirs for enabling predictive simulation
- An adaptive multi-element generalized polynomial chaos method for stochastic differential equations
- Numerical methods for multiscale elliptic problems
- A Bayesian analysis of some nonparametric problems
- A Hierarchical Multiscale Method for Two-Phase Flow Based upon Mixed Finite Elements and Nonuniform Coarse Grids
- Some matrix-variate distribution theory: Notational considerations and a Bayesian application
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- A mixed multiscale finite element method for elliptic problems with oscillating coefficients
- Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification
- Solution of inverse problems with limited forward solver evaluations: a Bayesian perspective
- An Introduction to the Numerics of Flow in Porous Media using Matlab
- On Estimation of a Probability Density Function and Mode
- Order-Based Dependent Dirichlet Processes
- Variational inference for Dirichlet process mixtures
