COMMUNICATIONS IN COMPUTATIONAL PHYSICS (CICP) 发表在物理问题的计算建模方面具有高科学价值的原创研究和调查论文。将展示所有物理科学中多物理和多尺度创新计算方法和建模的结果。

Communications in Computational Physics (CICP) publishes original research and survey papers of high scientific value in the field of computational modeling of physical problems. The journal showcases results of innovative computational methods and modeling across multiple physics disciplines, emphasizing multiphysics and multiscale approaches.

《Communications in Computational Physics》发表具有高科学价值的原创研究和调查论文，专注于物理问题的计算建模。期刊展示了在所有物理科学领域中，多物理和多尺度创新计算方法及建模的研究成果。

Communications in Computational Physics

Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty.In this work, we reveal the connection between the optimization difficulty of PINNs and activation functions.Specifically, we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct properties.Existing works usually choose activation functions by inefficient trial-and-error.To avoid the inefficient manual selection and to alleviate the optimization difficulty of PINNs, we introduce adaptive activation functions to search for the optimal function when solving different problems.We compare different adaptive activation functions and discuss their limitations in the context of PINNs.Furthermore, we propose to tailor the idea of learning combinations of candidate activation functions to the PINNs optimization, which has a higher requirement for the smoothness and diversity on learned functions.This is achieved by removing activation functions which cannot provide higher-order derivatives from the candidate set and incorporating elementary functions with different properties according to our prior knowledge about the PDE at hand.We further enhance the search space with adaptive slopes.The proposed adaptive activation function can be used to solve different PDE systems in an interpretable way.Its effectiveness is demonstrated on a series of benchmarks.Code is available at https://github.com/LeapLabTHU/AdaAFforPINNs.

Honghui Wang Honghui Wang

Lu Lu Lu Lu

Shiji Song Shiji Song

Gao Huang Gao Huang

Learning Specialized Activation Functions for Physics-Informed Neural Networks

In this paper, we propose a novel gradient recovery method for elliptic interface problem using body-fitted mesh in two dimension. Due to the lack of regularity of solution at interface, standard gradient recovery methods fail to give superconvergent results, and thus will lead to overrefinement when served as a posteriori error estimator. This drawback is overcome by designing an immersed gradient recovery operator in our method. We prove the superconvergence of this method for both mildly unstructured mesh and adaptive mesh, and present several numerical examples to verify the superconvergence and its robustness as a posteriori error estimator.

Hailong Guo

Xu Yang

Gradient Recovery for Elliptic Interface Problem: I. Body-Fitted Mesh

We propose a generalized space-time domain decomposition framework 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 ( XPINNs ), further pushes the boundaries of both PINNs as well as conservative PINNs (cPINNs), which is a recently proposed domain decomposition approach in the PINN framework tailored to conservation laws. Compared to PINN, the XPINN method has large representation and parallelization capacity due to the inherent property of deployment of multiple neural networks in the smaller subdomains. Unlike cPINN, XPINN can be extended to any type of PDEs. Moreover, the domain can be decomposed in any arbitrary way (in space and time), which is not possible in cPINN. Thus, XPINN offers both space and time parallelization, thereby reducing the training cost more effectively. In each subdomain, a separate neural network is employed with optimally selected hyperparameters, e.g., depth/width of the network, number and location of residual points, activation function, optimization method, etc. A deep network can be employed in a subdomain with complex solution, whereas a shallow neural network can be used in a subdomain with relatively simple and smooth solutions. We demonstrate the versatility of XPINN by solving both forward and inverse PDE problems, ranging from one-dimensional to three-dimensional problems, from time-dependent to time-independent problems, and from continuous to discontinuous problems, which clearly shows that the XPINN method is promising in many practical problems. The proposed XPINN method is the generalization of PINN and cPINN approaches, both in terms of applicability as well as domain decomposition approach, which efﬁciently lends itself to parallelized computation. The XPINN code will be available on https://github.com/AmeyaJagtap/XPINNs .

Ameya D. Jagtap & George Em Karniadakis

CiCP

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

扩展物理信息神经网络（XPINNs）：一种基于广义时空域分解的深度学习框架，用于非线性偏微分方程

Qiang He

Yiqian Cheng

Fengming Hu

Weifeng Huang null

Decai Li

A Comparative Study of Hydrodynamic Lattice Boltzmann Equation in Phase-Field-Based Multiphase Flow Models

基于相场法的多相流模型中流体动力学格子玻尔兹曼方程的比较研究

In the present manuscript, we consider the practical problem of wave interaction with a vertical wall. However, the novelty here consists in the fact that the wall can move horizontally due to a system of springs. The water wave evolution is described with the free surface potential flow model. Then, a semi-analytical numerical method is presented. It is based on a mapping technique and a finite difference scheme in the transformed domain. The idea is to pose the equations on a fixed domain. This method is thoroughly tested and validated in our study. By choosing specific values of spring parameters, this system can be used to damp (or in other words to extract the energy of) incident water waves.

Gayaz Khakimzyanov

Denys Dutykh

Numerical Modelling of Surface Water Wave Interaction with a Moving Wall

移动壁面与表面水波相互作用的数值模拟

Zhen Gao

Shuang Guo

Bao-Shan Wang and Yaguang Gu

High Order Bound- and Positivity-Preserving Finite Difference Affine-Invariant AWENO Scheme for the Five-Equation Model of Two-Medium Flows

高阶边界保持和正性保持有限差分仿射不变AWENO格式用于双介质流动的五方程模型

Tianwei Yu

Roman Fuchs and Ralf Hiptmair

Asymptotic-Preserving Discretization of Three-Dimensional Plasma Fluid Models

三维等离子体流体模型的渐近保持离散化

Gengjian Chen

Yuheng Ma null

Jiwei Zhang

Efficient Implementation of 3D FEM for Nonlocal Poisson Problem with Different Ball Approximation Strategies

非局部泊松问题的三维有限元高效实现及不同球面近似策略

Bert Mortier

Martine Baelmans null

Giovanni Samaey

A Study of Source Term Estimators in Coupled Finite-Volume/Monte-Carlo Methods with Applications to Plasma Edge Simulations in Nuclear Fusion: Track-Length and Next-Event Methods

耦合有限体积/蒙特卡罗方法中源项估计器的研究及其在核聚变等离子体边缘模拟中的应用：路径长度法和下一事件估计法

Xinyu Liu

Jie Shen null

Xiangxiong Zhang

A Simple GPU Implementation of Spectral-Element Methods for Solving 3D Poisson Type Equations on Rectangular Domains and its Applications

一种在矩形域上求解三维泊松型方程的简单GPU谱元方法实现及其应用

Abbreviation	Commun. Comput. Phys.
Journal Impact	2.47
Quartiles(Global)	PHYSICS, MATHEMATICAL(Q1)

ISSN	1815-2406, 1991-7120
h-index	67

Publication Information	Publisher: Global Science Press，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2007，Proportion of original research papers: 98.77%，Self Citation Rate:7.70%， Gold OA Rate: 0.00%
Average review cycle	网友分享经验：偏慢,4-8周
Average recruitment ratio	网友分享经验：较易

Communications in Computational Physics

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