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Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion

Journal of computational physics, 2019-04, Vol.383, p.166-195

2019

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Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification

Water resources research, 2019-05, Vol.55 (5), p.3856-3881

2019

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Multi-output local Gaussian process regression: Applications to uncertainty quantification

Journal of computational physics, 2012-07, Vol.231 (17), p.5718-5746

2012

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Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

Journal of computational physics, 2020-02, Vol.403, p.109056, Article 109056

2020

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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

Journal of computational physics, 2018-08, Vol.366, p.415-447

2018

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Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Journal of computational physics, 2019-04, Vol.383, p.125-147

2019

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Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method

Computer physics communications, 2014-11, Vol.185 (11), p.2885-2892

2014

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Transformers for modeling physical systems

Neural networks, 2022-02, Vol.146, p.272-289

2022

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A Bayesian inference approach to the inverse heat conduction problem

International journal of heat and mass transfer, 2004-08, Vol.47 (17), p.3927-3941

2004

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Predicting low-thermal-conductivity Si-Ge nanowires with a modified cluster expansion method

Physical review. B, Condensed matter and materials physics, 2015-02, Vol.91 (5), Article 054105

2015

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Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems

Journal of computational physics, 2022-04, Vol.455, p.111008, Article 111008

2022

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A non-intrusive stochastic Galerkin approach for modeling uncertainty propagation in deformation processes

Computers & structures, 2007-03, Vol.85 (5), p.244-254

2007

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Hierarchical Bayesian models for inverse problems in heat conduction

Inverse problems, 2005-02, Vol.21 (1), p.183-206

2005

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Sparse grid collocation schemes for stochastic natural convection problems

Journal of computational physics, 2007-07, Vol.225 (1), p.652-685

2007

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Control of macrosegregation during the solidification of alloys using magnetic fields

International journal of heat and mass transfer, 2006-12, Vol.49 (25), p.4850-4866

2006

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Modeling the growth and interaction of multiple dendrites in solidification using a level set method

Journal of computational physics, 2007-09, Vol.226 (1), p.131-155

2007

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Design of microstructure-sensitive properties in elasto-viscoplastic polycrystals using multi-scale homogenization

International journal of plasticity, 2006-10, Vol.22 (10), p.1799-1824

2006

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

Journal of computational physics, 2019-10, Vol.394, p.56-81

2019

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Uncertainty propagation in finite deformations––A spectral stochastic Lagrangian approach

Computer methods in applied mechanics and engineering, 2006-04, Vol.195 (19), p.2289-2312

2006

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Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems

Computer physics communications, 2018-09, Vol.230, p.70-88

2018

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Parallel probabilistic graphical model approach for nonparametric Bayesian inference

Journal of computational physics, 2018-11, Vol.372, p.546-563

2018

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Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media

Water resources research, 2019-01, Vol.55 (1), p.703-728

2019

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Solving inverse problems using conditional invertible neural networks

Journal of computational physics, 2021-05, Vol.433 (C), p.110194, Article 110194

2021

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A stochastic optimization approach to coarse-graining using a relative-entropy framework

The Journal of chemical physics, 2013-01, Vol.138 (4), p.044313-044313

2013

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The continuum sensitivity method for the computational design of three-dimensional deformation processes

Computer methods in applied mechanics and engineering, 2006-10, Vol.195 (48), p.6822-6842

2006

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Structured Bayesian Gaussian process latent variable model: Applications to data-driven dimensionality reduction and high-dimensional inversion

Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification

Multi-output local Gaussian process regression: Applications to uncertainty quantification

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks

Bayesian uncertainty quantification in the evaluation of alloy properties with the cluster expansion method

Transformers for modeling physical systems

A Bayesian inference approach to the inverse heat conduction problem

Predicting low-thermal-conductivity Si-Ge nanowires with a modified cluster expansion method

Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems

A non-intrusive stochastic Galerkin approach for modeling uncertainty propagation in deformation processes

Hierarchical Bayesian models for inverse problems in heat conduction

Sparse grid collocation schemes for stochastic natural convection problems

Control of macrosegregation during the solidification of alloys using magnetic fields

Modeling the growth and interaction of multiple dendrites in solidification using a level set method

Design of microstructure-sensitive properties in elasto-viscoplastic polycrystals using multi-scale homogenization

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

Uncertainty propagation in finite deformations––A spectral stochastic Lagrangian approach

Efficient data-driven reduced-order models for high-dimensional multiscale dynamical systems

Parallel probabilistic graphical model approach for nonparametric Bayesian inference

Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media

Solving inverse problems using conditional invertible neural networks

A stochastic optimization approach to coarse-graining using a relative-entropy framework

The continuum sensitivity method for the computational design of three-dimensional deformation processes

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