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Communications in Computational Physics is a peer-reviewed journal published by Cambridge University Press, covering Advanced Numerical Methods in Computational Mathematics, Computational Fluid Dynamics and Aerodynamics, Lattice Boltzmann Simulation Studies.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Communications in Computational Physics 12 (2023) 45–58.
Formats any DOI in the closest standard style — Communications in Computational Physics has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Advanced Numerical Methods in Computational Mathematics Computational Fluid Dynamics and Aerodynamics Lattice Boltzmann Simulation Studies Fluid Dynamics and Turbulent Flows Gas Dynamics and Kinetic Theory |
| ISSN | 1815-2406 |
| Citation impact (2-yr) | 2.78 |
| h-index | 74 |
| i10-index | 874 |
| Total citations | 39,085 |
| Top institutions publishing here | Chinese Academy of Sciences |
| Journal website | www.global-sci.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Here we propose a generalized space-time domain decomposition approach for the physics-informed neural networks (PINNs) to solve nonlinear partial differential equations (PDEs) on arbitrary complex-geometry domains. The proposed framework, named eXtended PINNs ( X P I N N s ), further pushes the boundaries of both PINNs as well as conservative PINNs (cPINNs), which is…
This paper gives a systematic introduction to HMM, the heterogeneous multiscale methods, including the fundamental design principles behind the HMM philosophy and the main obstacles that have to be overcome when using HMM for a particular problem. This is illustrated by examples from several application areas, including complex fluids, micro-fluidics, solids, interface problems, stochastic problems,…
Abstract The interactions between incompressible fluid flows and immersed structures are nonlinear multi-physics phenomena that have applications to a wide range of scientific and engineering disciplines. In this article, we review representative numerical methods based on conforming and non-conforming meshes that are currently available for computing fluid-structure interaction problems, with an emphasis on some of…
This paper presents a review of the current state-of-the-art of numerical methods for stochastic computations. The focus is on efficient high-order methods suitable for practical applications, with a particular emphasis on those based on generalized polynomial chaos (gPC) methodology. The framework of gPC is reviewed, along with its Galerkin and collocation approaches for solving stochastic…
Abstract The computational work and storage of numerically solving the time fractional PDEs are generally huge for the traditional direct methods since they require total memory and work, where N T and N S represent the total number of time steps and grid points in space, respectively. To overcome this difficulty, we present an efficient…
