JOURNAL OF COMPUTATIONAL PHYSICS 彻底处理了物理问题的计算方面，介绍了物理学各个领域中出现的数学方程的数值求解技术。该杂志力求强调跨越学科界限的方法。《计算物理学杂志》还出版 4 页或更少的简短注释（包括图表、表格和参考文献，但不包括标题页）。对已在本期刊上发表的文章发表评论的致编辑的信件也将被考虑。笔记和信件都不应该有摘要。

The Journal of Computational Physics focuses on the computational aspects of physical problems, presenting numerical solution techniques for mathematical equations arising in various fields of physics. The journal emphasizes interdisciplinary approaches. Additionally, the Journal of Computational Physics accepts brief notes of up to 4 pages (including figures, tables, and references, but excluding the title page). Letters to the editor commenting on articles published in this journal will also be considered. Notes and letters should not include abstracts.

《Journal of Computational Physics》专注于物理问题的计算方法，介绍了在物理学各个领域中出现的数学方程的数值求解技术。该杂志强调跨学科的方法。此外，《Journal of Computational Physics》还接受最多4页的简短注释（包括图表、表格和参考文献，但不包括标题页）。对已在本期刊上发表的文章的评论信件也将被考虑。注释和信件不需要摘要。

Journal of Computational Physics

Daniel Z. Huang

Kailai Xu

Charbel Farhat

Eric Darve

Learning constitutive relations from indirect observations using deep neural networks

Abstract We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct types of algorithms, namely continuous time and discrete time models. The first type of models forms a new family of data-efficient spatio-temporal function approximators, while the latter type allows the use of arbitrarily accurate implicit Runge–Kutta time stepping schemes with unlimited number of stages. The effectiveness of the proposed framework is demonstrated through a collection of classical problems in fluids, quantum mechanics, reaction–diffusion systems, and the propagation of nonlinear shallow-water waves.

M. Raissi

P. Perdikaris

G.E. Karniadakis

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

物理信息神经网络：一种用于求解涉及非线性偏微分方程的正向和逆问题的深度学习框架

Two versions of flux corrected transport and two versions of total variation diminishing schemes are tested for several one- and two-dimensional hydrodynamic and magnetohydrodynamic problems. Two of the schemes, YDFCT and TVDLF are tested extensively for the first time. The results give an insight into the limitations of the methods, their relative strengths and weaknesses. Some subtle points of the algorithms and the effects of selecting different options for certain methods are emphasised.

Gábor Tóth

Dušan Odstrčil

Comparison of Some Flux Corrected Transport and Total Variation Diminishing Numerical Schemes for Hydrodynamic and Magnetohydrodynamic Problems

一些通量校正传输和总变差减小数值方案在流体动力学和磁流体动力学问题中的比较

This paper describes a new concept for the implementation of the direct simulation Monte Carlo (DSMC) method. It uses a localized data structure based on a computational cell to achieve high performance, especially on workstation processors, which can also be used in parallel. Since the data structure makes it possible to freely assign any cell to any processor, a domain decomposition can be found with equal calculation load on each processor while maintaining minimal communication among the nodes. Further, the new implementation strictly separates physical modeling, geometrical issues, and organizational tasks to achieve high maintainability and to simplify future enhancements. Three example flow configurations are calculated with the new implementation to demonstrate its generality and performance. They include a flow through a diverging channel using an adapted unstructured triangulated grid, a flow around a planetary probe, and an internal flow in a contactor used in plasma physics. The results are validated either by comparison with results obtained from other simulations or by comparison with experimental data. High performance on an IBM SP2 system is achieved if problem size and number of parallel processors are adapted accordingly. On 400 nodes, DSMC calculations with more than 100 million particles are possible.

Stefan Dietrich

Iain D. Boyd

Scalar and Parallel Optimized Implementation of the Direct Simulation Monte Carlo Method

直接模拟蒙特卡罗方法的标量与并行优化实现

In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it possible to robustly address multi-objective and multi-scale problems. BPINNs are a popular framework for data assimilation, combining the constraints of Uncertainty Quantification (UQ) and Partial Differential Equation (PDE). The relative weights of the BPINN target distribution terms are directly related to the inherent uncertainty in the respective learning tasks. Yet, they are usually manually set a-priori, that can lead to pathological behavior, stability concerns, and to conflicts between tasks which are obstacles that have deterred the use of BPINNs for inverse problems with multi-scale dynamics. The present weighting strategy automatically tunes the weights by considering the multi-task nature of target posterior distribution. We show that this remedies the failure modes of BPINNs and provides efficient exploration of the optimal Pareto front. This leads to better convergence and stability of BPINN training while reducing sampling bias. The determined weights moreover carry information about task uncertainties, reflecting noise levels in the data and adequacy of the PDE model. We demonstrate this in numerical experiments in Sobolev training, and compare them to analytically $\epsilon$-optimal baseline, and in a multi-scale Lokta-Volterra inverse problem. We eventually apply this framework to an inpainting task and an inverse problem, involving latent field recovery for incompressible flow in complex geometries.

Sarah Perez

Suryanarayana Maddu

Ivo F. Sbalzarini

Philippe Poncet

Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

M. Srati

A. Oulmelk

L. Afraites

A. Hadri

Mahmoud A. Zaky

A.S. Hendy

Learning primal-dual approach for space-dependent diffusion coefficient identification in fractional diffusion equations

分数阶扩散方程中空间相关扩散系数识别的原始对偶学习方法

Yunzhu Cai

Jiawei Wan

Ahsan Kareem

On convergence of implicit Runge-Kutta methods for the incompressible Navier-Stokes equations with unsteady inflow

关于非定常流入下不可压缩Navier-Stokes方程的隐式Runge-Kutta方法的收敛性

Alexander Rothkopf

W.A. Horowitz

Jan Nordström

Exact symmetry conservation and automatic mesh refinement in discrete initial boundary value problems

离散初边值问题中的精确对称保持与自动网格细化

Amit N. Subrahmanya

Andrey A. Popov

Adrian Sandu

Ensemble Variational Fokker-Planck Methods for Data Assimilation

数据同化中的集合变分福克-普朗克方法

Salar Zamani Salimi

Aritra Mukherjee

Marica Pelanti

Luca Brandt

A low Mach number diffuse-interface model for multicomponent two-phase flows with phase change

低马赫数多组分两相流动相变扩散界面模型

Abbreviation	J. Comput. Phys.
Journal Impact	3.67
Quartiles(Global)	PHYSICS, MATHEMATICAL(Q1)

ISSN	0021-9991, 1090-2716
h-index	288
Top Journals	Yes

Publication Information	Publisher: Academic Press Inc.，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1966，Proportion of original research papers: 99.88%，Self Citation Rate:13.20%， Gold OA Rate: 40.78%
Average review cycle	网友分享经验：平均6.5个月来源Elsevier官网：平均17.2周
Average recruitment ratio	网友分享经验：约25%来源Elsevier官网：37%

Journal of Computational Physics

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