工程计算方法档案为传播计算工程研究和先进实践的成果提供了一个活跃的论坛，特别强调力学及其相关领域。该杂志在选定领域独家发表扩展的最先进的评论，这是该杂志的一个独特之处。发表在该期刊上的评论不仅提供了对当前文献的调查，而且还对该主题的全部复杂性进行了批判性阐述。主题的部分列表包括建模；求解技术和计算方法在各个领域的应用（例如，液体和气体动力学、固体和结构力学、生物力学等）；与实施有限元和边界元方法相关的变分公式和数值算法；有限差分法和有限体积法；和其他基本的计算方法。

The Archives of Computational Methods in Engineering provides a vibrant forum for disseminating the results of computational engineering research and advanced practices, with a particular emphasis on mechanics and related fields. This journal uniquely publishes extended state-of-the-art reviews, which not only survey the current literature but also critically elaborate on the complexities of the subject. Some of the topics covered include modeling, solution techniques, and the application of computational methods in various fields such as fluid and gas dynamics, solid and structural mechanics, and biomechanics; variational formulations and numerical algorithms related to the implementation of finite element and boundary element methods; finite difference methods and finite volume methods; and other fundamental computational methods.

《Archives of Computational Methods in Engineering》是一个活跃的论坛，致力于传播计算工程研究和先进实践的成果，特别强调力学及其相关领域。该期刊独家发表扩展的前沿评论，这是其独特之处。这些评论不仅对当前文献进行了全面的调查，还对主题的复杂性进行了深入的批判性阐述。部分主题包括建模、求解技术以及计算方法在液体和气体动力学、固体和结构力学、生物力学等各个领域的应用；与实施有限元和边界元方法相关的变分公式和数值算法；有限差分法和有限体积法；以及其他基本的计算方法。

Archives of Computational Methods in Engineering

In recent years, physics-informed neural networks (PINN) have been used to solve stiff-PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D problems, especially, problems with conflicting boundary conditions at adjacent edges and corners. These problems have discontinuous solutions at edges and corners that are difficult to learn for neural networks with a continuous activation function. In this review paper, we have investigated various PINN frameworks that are designed to solve stiff-PDEs. We took two heat conduction problems (2D and 3D) with a discontinuous solution at corners as test cases. We investigated these problems with a number of PINN frameworks, discussed and analysed the results against the FEM solution. It appears that PINNs provide a more general platform for parameterisation compared to conventional solvers. Thus, we have investigated the 2D heat conduction problem with parametric conductivity and geometry separately. We also discuss the challenges associated with PINNs and identify areas for further investigation.

Prakhar Sharma

Llion Evans

Michelle Tindall

Perumal Nithiarasu

Arch Computat Methods Eng

Stiff-PDEs and Physics-Informed Neural Networks

刚性偏微分方程与物理信息神经网络

Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.

Dimitrios Angelis

Filippos Sofos

Theodoros E. Karakasidis

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

物理科学中的人工智能：符号回归的趋势与展望

Shubhkirti Sharma

Vijay Kumar

A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future

多目标优化技术综合评述：过去、现在与未来

Smoothed particle hydrodynamics (SPH) is a meshfree particle method based on Lagrangian formulation, and has been widely applied to different areas in engineering and science. This paper presents an overview on the SPH method and its recent developments, including (1) the need for meshfree particle methods, and advantages of SPH, (2) approximation schemes of the conventional SPH method and numerical techniques for deriving SPH formulations for partial differential equations such as the Navier-Stokes (N-S) equations, (3) the role of the smoothing kernel functions and a general approach to construct smoothing kernel functions, (4) kernel and particle consistency for the SPH method, and approaches for restoring particle consistency, (5) several important numerical aspects, and (6) some recent applications of SPH. The paper ends with some concluding remarks.

M. B. Liu

G. R. Liu

Smoothed Particle Hydrodynamics (SPH): an Overview and Recent Developments

光滑粒子流体动力学（SPH）：综述与最新进展

Mahmoud A. El-Emam

Ling Zhou

Weidong Shi

Chen Han

Ling Bai

Ramesh Agarwal

Theories and Applications of CFD–DEM Coupling Approach for Granular Flow: A Review

颗粒流CFD-DEM耦合方法的理论与应用综述

Ferzat Anka

Nazim Agaoglu

Sajjad Nematzadeh

Mahsa Torkamanian-afshar

Farhad Soleimanian Gharehchopogh

Advances in Artificial Rabbits Optimization: A Comprehensive Review

人工兔子优化进展：综合评述

Abstract Finite element model updating (FEMU) is an advanced inverse parameter identification method capable of identifying multiple parameters in a material model through one or a few well-designed material tests. The method has become more mature thanks to the widespread use of full-field measurement techniques, such as digital image correlation. Proper application of FEMU requires extensive expertise. This paper offers a review of FEMU and a guide to practice. It also presents FEMU-DIC, an open-source software package. We conclude by discussing the challenges and opportunities in this field with the intent of inspiring future research.

Bin Chen

Bojan Starman

Miroslav Halilovič

Lars A. Berglund

Sam Coppieters

Finite Element Model Updating for Material Model Calibration: A Review and Guide to Practice

材料模型校准的有限元模型更新：综述与实践指南

Binita Dash

Trupti Ranjan Mahapatra

Punyapriya Mishra

Debadutta Mishra

S. R. Mahmoud

A Review of Computational Methods for Vibroacoustic Analysis of Advanced Material Structures

先进材料结构振动声学分析计算方法综述

Eric Parish

Masayuki Yano

Irina Tezaur

Traian Iliescu

Residual-Based Stabilized Reduced-Order Models of the Transient Convection–Diffusion–Reaction Equation Obtained Through Discrete and Continuous Projection

基于残差的稳定化降阶模型：通过离散和连续投影获得的瞬态对流-扩散-反应方程

Nitin Kishore Rawat

Naman Jain

Abhishek Kumar Mishra

Akarsh Verma

Atomistic-Scale Simulations on Grain Boundary Migration Mechanisms Involved in Metals and Alloys: A Critical Review

金属与合金中晶界迁移机制的原子尺度模拟：关键综述

Abbreviation	Arch Computat Methods Eng
Journal Impact	9.84
Quartiles(Global)	MATHEMATICS, INTERDISCIPLINARY APPLICATIONS(Q1)

ISSN	1134-3060, 1886-1784
h-index	92
Top Journals	Yes

Publication Information	Publisher: Springer Netherlands，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1994，Proportion of original research papers: 14.37%，Self Citation Rate:4.10%， Gold OA Rate: 15.15%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：较易

Archives of Computational Methods in Engineering

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