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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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The scalar auxiliary variable (SAV) approach for gradient flows

Journal of computational physics, 2018-01, Vol.353, p.407-416

2018

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

Journal of computational physics, 2020-03, Vol.404 (C), p.109136, Article 109136

2020

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Adaptive numerical algorithms in space weather modeling

Journal of computational physics, 2012-02, Vol.231 (3), p.870-903

2012

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Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Journal of computational physics, 2023-03, Vol.477 (C), p.111902, Article 111902

2023

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When and why PINNs fail to train: A neural tangent kernel perspective

Journal of computational physics, 2022-01, Vol.449, p.110768, Article 110768

2022

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NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

Journal of computational physics, 2021-02, Vol.426 (C), p.109951, Article 109951

2021

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DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks

Journal of computational physics, 2021-07, Vol.436 (C), p.110296, Article 110296

2021

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

Journal of computational physics, 2020-07, Vol.413, p.109456, Article 109456

2020

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Machine learning strategies for systems with invariance properties

Journal of computational physics, 2016-08, Vol.318 (C), p.22-35

2016

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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Journal of computational physics, 2019-02, Vol.378, p.686-707

2019

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A review of definitions of fractional derivatives and other operators

Journal of computational physics, 2019-07, Vol.388, p.195-208

2019

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Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Journal of computational physics, 2015-03, Vol.285 (C), p.316-330

2015

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

Journal of computational physics, 2021-01, Vol.425 (C), p.109913, Article 109913

2021

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A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

Journal of computational physics, 2017-02, Vol.330 (C), p.192-220

2017

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Adversarial uncertainty quantification in physics-informed neural networks

Journal of computational physics, 2019-10, Vol.394 (C), p.136-152

2019

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An efficient class of WENO schemes with adaptive order

Journal of computational physics, 2016-12, Vol.326, p.780-804

2016

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DGM: A deep learning algorithm for solving partial differential equations

Journal of computational physics, 2018-12, Vol.375, p.1339-1364

2018

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Long-time integration of parametric evolution equations with physics-informed DeepONets

Journal of computational physics, 2023-02, Vol.475, p.111855, Article 111855

2023

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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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On the role of polynomials in RBF-FD approximations: I. Interpolation and accuracy

Journal of computational physics, 2016-09, Vol.321, p.21-38

2016

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Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

Journal of computational physics, 2018-12, Vol.375, p.565-588

2018

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Self-adaptive physics-informed neural networks

Journal of computational physics, 2023-02, Vol.474, p.111722, Article 111722

2023

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Machine learning of linear differential equations using Gaussian processes

Journal of computational physics, 2017-11, Vol.348, p.683-693

2017

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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Journal of computational physics, 2020-03, Vol.404, p.108973, Article 108973

2020

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

The scalar auxiliary variable (SAV) approach for gradient flows

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

Adaptive numerical algorithms in space weather modeling

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

When and why PINNs fail to train: A neural tangent kernel perspective

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

Machine learning strategies for systems with invariance properties

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

A review of definitions of fractional derivatives and other operators

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

Adversarial uncertainty quantification in physics-informed neural networks

An efficient class of WENO schemes with adaptive order

DGM: A deep learning algorithm for solving partial differential equations

Long-time integration of parametric evolution equations with physics-informed DeepONets

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

On the role of polynomials in RBF-FD approximations: I. Interpolation and accuracy

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

Self-adaptive physics-informed neural networks

Machine learning of linear differential equations using Gaussian processes

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

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