Communications in Computational Physics

Papers
(The H4-Index of Communications in Computational Physics is 19. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)
ArticleCitations
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations259
Dying ReLU and Initialization: Theory and Numerical Examples109
On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs98
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks83
Natural Convection Heat Transfer in a Porous Cavity with Sinusoidal Temperature Distribution Using Cu/Water Nanofluid: Double MRT Lattice Boltzmann Method70
Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains50
Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks50
A Second-Order Scheme with Nonuniform Time Steps for a Linear Reaction-Subdiffusion Problem48
A Third Order BDF Energy Stable Linear Scheme for the No-Slope-Selection Thin Film Model38
Deep Network Approximation Characterized by Number of Neurons37
A Positivity-Preserving Second-Order BDF Scheme for the Cahn-Hilliard Equation with Variable Interfacial Parameters36
Finite Neuron Method and Convergence Analysis31
Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions31
A Novel Full-Euler Low Mach Number IMEX Splitting22
Machine Learning and Computational Mathematics22
An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems21
Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units21
DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion20
Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains19
Target-Oriented Inversion of Time-Lapse Seismic Waveform Data19
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