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hp-VPINNs: Variational physics-informed neural networks with domain decomposition

Computer methods in applied mechanics and engineering, 2021-02, Vol.374 (C), p.113547, Article 113547

2021

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fPINNs: Fractional Physics-Informed Neural Networks

SIAM journal on scientific computing, 2019-01, Vol.41 (4), p.A2603-A2626

2019

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Fractional Sturm–Liouville eigen-problems: Theory and numerical approximation

Journal of computational physics, 2013-11, Vol.252, p.495-517

2013

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Physics-informed neural networks for high-speed flows

Computer methods in applied mechanics and engineering, 2020-03, Vol.360, p.112789, Article 112789

2020

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Systems biology informed deep learning for inferring parameters and hidden dynamics

PLoS computational biology, 2020-11, Vol.16 (11), p.e1007575-e1007575

2020

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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Computer methods in applied mechanics and engineering, 2022-04, Vol.393 (C), p.114823, Article 114823

2022

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Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks

SIAM journal on scientific computing, 2021-01, Vol.43 (3), p.B811-B830

2021

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Physics-Informed Neural Networks for Heat Transfer Problems

Journal of heat transfer, 2021-06, Vol.143 (6)

2021

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Exponentially accurate spectral and spectral element methods for fractional ODEs

Journal of computational physics, 2014-01, Vol.257, p.460-480

2014

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

SIAM journal on scientific computing, 2020-01, Vol.42 (1), p.A292-A317

2020

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Fractional spectral collocation methods for linear and nonlinear variable order FPDEs

Journal of computational physics, 2015-07, Vol.293 (C), p.312-338

2015

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Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

Proceedings of the Royal Society. A, Mathematical, physical, and engineering sciences, 2020-07, Vol.476 (2239), p.20200334-20200334

2020

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Physics-informed neural networks (PINNs) for fluid mechanics: a review

Acta mechanica Sinica, 2021-12, Vol.37 (12), p.1727-1738

English ed., 2021

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Non-invasive inference of thrombus material properties with physics-informed neural networks

Computer methods in applied mechanics and engineering, 2021-03, Vol.375, p.113603, Article 113603

2021

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Fractional Spectral Collocation Method

SIAM journal on scientific computing, 2014-01, Vol.36 (1), p.A40-A62

2014

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Operator learning for predicting multiscale bubble growth dynamics

The Journal of chemical physics, 2021-03, Vol.154 (10), p.104118-104118

2021

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A three-dimensional phase-field model for multiscale modeling of thrombus biomechanics in blood vessels

PLoS computational biology, 2020-04, Vol.16 (4), p.e1007709

2020

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Science (American Association for the Advancement of Science), 2020-02, Vol.367 (6481), p.1026-1030

2020

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Numerical Methods for Stochastic Partial Differential Equations with White Noise

1st ed. 2017 edition., 2017

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A Petrov–Galerkin spectral element method for fractional elliptic problems

Computer methods in applied mechanics and engineering, 2017-09, Vol.324, p.512-536

2017

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Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

The Journal of chemical physics, 2015-12, Vol.143 (24), p.243128-243128

2015

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Petrov--Galerkin and Spectral Collocation Methods for Distributed Order Differential Equations

SIAM journal on scientific computing, 2017-01, Vol.39 (3), p.A1003-A1037

2017

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Second-order approximations for variable order fractional derivatives: Algorithms and applications

Journal of computational physics, 2015-07, Vol.293, p.184-200

2015

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Biomechanics of red blood cells in human spleen and consequences for physiology and disease

Proceedings of the National Academy of Sciences - PNAS, 2016-07, Vol.113 (28), p.7804-7809

2016

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Computing the non-Markovian coarse-grained interactions derived from the Mori–Zwanzig formalism in molecular systems: Application to polymer melts

The Journal of chemical physics, 2017-01, Vol.146 (1), p.014104-014104

2017

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hp-VPINNs: Variational physics-informed neural networks with domain decomposition

fPINNs: Fractional Physics-Informed Neural Networks

Fractional Sturm–Liouville eigen-problems: Theory and numerical approximation

Physics-informed neural networks for high-speed flows

Systems biology informed deep learning for inferring parameters and hidden dynamics

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks

Physics-Informed Neural Networks for Heat Transfer Problems

Exponentially accurate spectral and spectral element methods for fractional ODEs

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

Fractional spectral collocation methods for linear and nonlinear variable order FPDEs

Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

Physics-informed neural networks (PINNs) for fluid mechanics: a review

Non-invasive inference of thrombus material properties with physics-informed neural networks

Fractional Spectral Collocation Method

Operator learning for predicting multiscale bubble growth dynamics

A three-dimensional phase-field model for multiscale modeling of thrombus biomechanics in blood vessels

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Numerical Methods for Stochastic Partial Differential Equations with White Noise

A Petrov–Galerkin spectral element method for fractional elliptic problems

Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

Petrov--Galerkin and Spectral Collocation Methods for Distributed Order Differential Equations

Second-order approximations for variable order fractional derivatives: Algorithms and applications

Biomechanics of red blood cells in human spleen and consequences for physiology and disease

Computing the non-Markovian coarse-grained interactions derived from the Mori–Zwanzig formalism in molecular systems: Application to polymer melts

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