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Vol. 28 (2020), Issue 5

Vol. 28 (2020), Issue 5

Journal Issue

Year:    2020

Communications in Computational Physics, Vol. 28 (2020)

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Preface

Journal Article

Preface

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : p. 0

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Machine Learning and Computational Mathematics

Journal Article

Machine Learning and Computational Mathematics

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1639–1670

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Dying ReLU and Initialization: Theory and Numerical Examples

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Dying ReLU and Initialization: Theory and Numerical Examples

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1671–1706

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Finite Neuron Method and Convergence Analysis

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Finite Neuron Method and Convergence Analysis

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1707–1745

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Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

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Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1746–1767

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Deep Network Approximation Characterized by Number of Neurons

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Deep Network Approximation Characterized by Number of Neurons

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1768–1811

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Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression

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Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1812–1837

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Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks

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Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1838–1885

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A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

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A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1886–1906

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Random Batch Algorithms for Quantum Monte Carlo Simulations

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Random Batch Algorithms for Quantum Monte Carlo Simulations

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1907–1936

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High-Dimensional Nonlinear Multi-Fidelity Model with Gradient-Free Active Subspace Method

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High-Dimensional Nonlinear Multi-Fidelity Model with Gradient-Free Active Subspace Method

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1937–1969

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Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains

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Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 1970–2001

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

Journal Article

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 2002–2041

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On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

Journal Article

On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 2042–2074

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Convolution Neural Network Shock Detector for Numerical Solution of Conservation Laws

Journal Article

Convolution Neural Network Shock Detector for Numerical Solution of Conservation Laws

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 2075–2108

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Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations

Journal Article

Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 2109–2138

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Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains

Journal Article

Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 2139–2157

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Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

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Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 2158–2179

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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

Communications in Computational Physics, Vol. 28 (2020), Iss. 5 : pp. 2180–2205

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