Search Results - "Communications in computational physics"
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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
Published in Communications in computational physics (01-11-2020)“…Here we propose a generalized space-time domain decomposition approach for the physics-informed neural networks (PINNs) to solve nonlinear partial differential…”
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On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs
Published in Communications in computational physics (01-11-2020)“…Physics informed neural networks (PINNs) are deep learning based techniques for solving partial differential equations (PDEs) encountered in computational…”
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Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
Published in Communications in computational physics (01-11-2020)Get full text
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Fast Evaluation of the Caputo Fractional Derivative and its Applications to Fractional Diffusion Equations
Published in Communications in computational physics (01-03-2017)“…The computational work and storage of numerically solving the time fractional PDEs are generally huge for the traditional direct methods since they require…”
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Deep Network Approximation Characterized by Number of Neurons
Published in Communications in computational physics (01-11-2020)Get full text
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Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
Published in Communications in computational physics (01-11-2020)Get full text
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Dying ReLU and Initialization: Theory and Numerical Examples
Published in Communications in computational physics (01-11-2020)Get full text
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Deep Potential: A General Representation of a Many-Body Potential Energy Surface
Published in Communications in computational physics (01-03-2018)“…Not provided…”
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Analysis of L1-Galerkin FEMs for Time-Fractional Nonlinear Parabolic Problems
Published in Communications in computational physics (2018)Get full text
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A second-order energy stable BDF numerical scheme for the Cahn-Hilliard equation
Published in Communications in computational physics (01-02-2018)Get full text
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Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
Published in Communications in computational physics (01-05-2021)Get full text
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Solving Allen-Cahn and Cahn-Hilliard Equations Using the Adaptive Physics Informed Neural Networks
Published in Communications in computational physics (01-03-2021)Get full text
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Fast Evaluation of the Caputo Fractional Derivative and its Applications to Fractional Diffusion Equations: A Second-Order Scheme
Published in Communications in computational physics (01-10-2017)“…The fractional derivatives include nonlocal information and thus their calculation requires huge storage and computational cost for long time simulations. We…”
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Numerical Methods for Fluid-Structure Interaction — A Review
Published in Communications in computational physics (01-08-2012)“…The interactions between incompressible fluid flows and immersed structures are nonlinear multi-physics phenomena that have applications to a wide range of…”
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A Second-Order Scheme with Nonuniform Time Steps for a Linear Reaction-Subdiffusion Problem
Published in Communications in computational physics (2021)Get full text
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Phase-Field Models for Multi-Component Fluid Flows
Published in Communications in computational physics (01-09-2012)“…In this paper, we review the recent development of phase-field models and their numerical methods for multi-component fluid flows with interfacial phenomena…”
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A Finite-Volume Method for Nonlinear Nonlocal Equations with a Gradient Flow Structure
Published in Communications in computational physics (01-01-2015)“…We propose a positivity preserving entropy decreasing finite volume scheme for nonlinear nonlocal equations with a gradient flow structure. These properties…”
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Particle based gPC methods for mean-field models of swarming with uncertainty
Published in Communications in computational physics (2019)Get full text
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