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 2020-03-01 to 2024-03-01.)
ArticleCitations
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations248
Dying ReLU and Initialization: Theory and Numerical Examples105
On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs97
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks76
Natural Convection Heat Transfer in a Porous Cavity with Sinusoidal Temperature Distribution Using Cu/Water Nanofluid: Double MRT Lattice Boltzmann Method70
Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks47
Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains46
A Second-Order Scheme with Nonuniform Time Steps for a Linear Reaction-Subdiffusion Problem44
A Third Order BDF Energy Stable Linear Scheme for the No-Slope-Selection Thin Film Model37
A Positivity-Preserving Second-Order BDF Scheme for the Cahn-Hilliard Equation with Variable Interfacial Parameters36
Deep Network Approximation Characterized by Number of Neurons36
Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions31
Finite Neuron Method and Convergence Analysis30
A Novel Full-Euler Low Mach Number IMEX Splitting22
Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units21
Machine Learning and Computational Mathematics20
An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems19
Target-Oriented Inversion of Time-Lapse Seismic Waveform Data19
DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion19
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