Journal of Computational Science最新文献

Corrosion-induced multiscale damage behavior of ultrahigh strength steel: An integrated simulation and experiment study 超高强度钢腐蚀致多尺度损伤行为：模拟与试验相结合的研究

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-13 DOI: 10.1016/j.jocs.2025.102676

Weizheng Lu , Zouxueyin Wang , Libo Yu , Shaohua Xing , Andong Wang , Yong Zhang , Jia Li , Qihong Fang

{"title":"Corrosion-induced multiscale damage behavior of ultrahigh strength steel: An integrated simulation and experiment study","authors":"Weizheng Lu , Zouxueyin Wang , Libo Yu , Shaohua Xing , Andong Wang , Yong Zhang , Jia Li , Qihong Fang","doi":"10.1016/j.jocs.2025.102676","DOIUrl":"10.1016/j.jocs.2025.102676","url":null,"abstract":"<div><div>Corrosion is an aggravating problem to cause the premature failure of structure materials, ultimately impacting the safety and operational expenses of equipment. However, the corrosion-induced multiscale damage evolution in the ultrahigh-strength steel is not clearly revealed from atomic scale to macroscopic scale. Here, corrosion-induced multiscale damage mechanism of ultrahigh strength steel plate is investigated using the experiments combined with multiscale simulation, including molecular dynamic simulation, cellular automaton simulation, and phase field finite element method. The experiment shows that the high angle grain boundaries are particularly vulnerable to corrosion, grain refinement takes place during the process of corrosion, and the exposed surface displays significant cracks in the surface of plate. From molecular dynamic simulation, the thickness of the passivation film and the corrosion rate go up with the increasing temperature, which accelerates the early passivation. The corrosion-induced cracks promote the local healing of surface roughness, leading to low strain softening at the nanoscale. By cellular automaton simulation, the passivation film, formed by the corrosion products, serves to hinder the anodic dissolution of the matrix, thereby reducing the average depth of the corrosion pits. Through phase field finite element simulation, the concentration of local strain plays a crucial role in accelerating the rupture rate of the passive film and increasing the corrosion rate at the tip of a pit. Additionally, strong local strains have a significant impact on the longitudinal advancement of corrosion, leading to the progression from a corrosion pit to a crack. These findings not only give a deep understanding of the corrosion-induced cracking behavior, but also provide valuable insights for the development of steel plate with enhanced mechanical properties.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102676"},"PeriodicalIF":3.1,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Link Strength Prediction: Leveraging line graph transformations and neural networks 深度链接强度预测：利用线图转换和神经网络

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-12 DOI: 10.1016/j.jocs.2025.102661

Zhixin Ming , Jie Li , Jing Wang

{"title":"Deep Link Strength Prediction: Leveraging line graph transformations and neural networks","authors":"Zhixin Ming , Jie Li , Jing Wang","doi":"10.1016/j.jocs.2025.102661","DOIUrl":"10.1016/j.jocs.2025.102661","url":null,"abstract":"<div><div>Predicting link strengths in complex networks is a fundamental challenge, crucial for understanding network dynamics and optimizing real-world applications. Traditional approaches often rely on shallow structural features, limiting their ability to model intricate dependencies. To address these limitations, we propose Deep Link Strength Prediction (DLSP), a novel framework that integrates line graph transformations with graph convolutional networks (GCNs) to enhance the predictive capability of link weight estimation. DLSP redefines the task by transforming edge-centric information into node-level representations, facilitating effective learning of complex structural patterns. DLSP follows a multi-phase approach: first, a localized subgraph around the target link is extracted and encoded using a weighted node labeling scheme, preserving local structural and attribute-driven properties. Next, the labeled subgraph undergoes a line graph transformation, mapping link dependencies into node representations, thereby enabling a structured embedding space. A GCN is then employed to extract rich hierarchical representations, capturing both micro and macro-level graph structures. Finally, these learned embeddings are passed through a dense neural network to estimate the target link strength, framing the problem as a continuous-valued regression task. Unlike existing methods that rely on handcrafted features or isolated node embeddings, DLSP explicitly models link dependencies through graph-aware transformations, leading to superior predictive performance. Extensive experiments conducted on six diverse network datasets demonstrate that DLSP consistently outperforms state-of-the-art methods, showcasing its robustness, scalability, and potential for real-world applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102661"},"PeriodicalIF":3.1,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fractional order malaria epidemic model: Qualitative and computational study to determine the dynamics for sensitivity prevalence 分数阶疟疾流行模型：确定敏感性流行动态的定性和计算研究

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-04 DOI: 10.1016/j.jocs.2025.102656

Muhammad Farman , Nezihal Gokbulut , Aamir Shehzad , Kottakkaran Sooppy Nisar , Evren Hincal , Aceng Sambas

{"title":"Fractional order malaria epidemic model: Qualitative and computational study to determine the dynamics for sensitivity prevalence","authors":"Muhammad Farman , Nezihal Gokbulut , Aamir Shehzad , Kottakkaran Sooppy Nisar , Evren Hincal , Aceng Sambas","doi":"10.1016/j.jocs.2025.102656","DOIUrl":"10.1016/j.jocs.2025.102656","url":null,"abstract":"<div><div>In this study, we created a nonlinear mathematical model with eight compartments to understand the dynamics of malaria transmission in North Cyprus region using the Caputo fractional operator. Because of their memory and genetic features, fractional-order models are regarded to be more adaptable than integer-order models. To explore the malaria compartmental model, we use the stability theory of fractional-order differential equations with the Caputo operator. A full explanation of the proposed model’s qualitative and quantitative analysis is offered, as well as a brief overview of its essential aspects and a theoretical evaluation. The Lipschitz criterion and well-known fixed point theorems are used to prove the existence and uniqueness of solutions. In addition to establishing equilibrium points, sensitivity analysis of reproductive number parameters is carried out. The proposed system has been validated in terms of Ulam–Hyers–Rassias. To deal with chaotic circumstances a linear feedback control strategy directs system dynamics near equilibrium points. To verify the existence of bifurcation, we apply bifurcation principles. The study uses numerical methodology based on Newton polynomial interpolation method to graphically model the solutions. The study analyzes system behavior by investigating parameter alterations at various fractional orders while retaining model stability. The long-term memory effect, represented by the Caputo fractional order derivative, has no influence on steady point stability, but solutions get closer to equilibrium faster at higher fractional-orders.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102656"},"PeriodicalIF":3.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data classification with dynamically growing and shrinking neural networks 动态增长和收缩神经网络的数据分类

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-01 DOI: 10.1016/j.jocs.2025.102660

Szymon Świderski , Agnieszka Jastrzębska

{"title":"Data classification with dynamically growing and shrinking neural networks","authors":"Szymon Świderski , Agnieszka Jastrzębska","doi":"10.1016/j.jocs.2025.102660","DOIUrl":"10.1016/j.jocs.2025.102660","url":null,"abstract":"<div><div>The issue of data-driven neural network model construction is one of the core problems in the domain of Artificial Intelligence. A standard approach assumes a fixed architecture with trainable weights. A conceptually more advanced assumption is that we not only train the weights but also find out the optimal model architecture. We present a new method that realizes just that. This article is an extended version of our conference paper titled “Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search (Świderski and Jastrzebska, 2024). In the paper, we show in detail how to create a neural network with a procedure that allows dynamic shrinking and growing of the model while it is being trained. The decision-making mechanism for the architectural design is governed by the Monte Carlo tree search procedure, which simulates network behavior and allows comparing several candidate architecture changes to choose the best one. The proposed method was validated using both visual and time series datasets, demonstrating its particular effectiveness in multivariate time series classification. This is attributed to the architecture’s ability to adapt dynamically, allowing independent modifications for each time series. To enhance the reproducibility of our method, we publish open-source code of the proposed method. It was prepared in Python. Experimental evaluations in visual pattern and multivariate time series classification tasks revealed highly promising performance, underscoring the method’s robustness and adaptability.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102660"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast and power efficient GPU-based explicit elastic wave propagation analysis by low-ordered orthogonal voxel finite element with INT8 Tensor Cores 基于INT8张量核低阶正交体素有限元的快速高效gpu显式弹性波传播分析

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-07-01 DOI: 10.1016/j.jocs.2025.102659

Tsuyoshi Ichimura , Kohei Fujita , Muneo Hori , Maddegedara Lalith

{"title":"Fast and power efficient GPU-based explicit elastic wave propagation analysis by low-ordered orthogonal voxel finite element with INT8 Tensor Cores","authors":"Tsuyoshi Ichimura , Kohei Fujita , Muneo Hori , Maddegedara Lalith","doi":"10.1016/j.jocs.2025.102659","DOIUrl":"10.1016/j.jocs.2025.102659","url":null,"abstract":"<div><div>There is a strong need for faster and more power-efficient explicit elastic wavefield simulations for large and complex three-dimensional media using a structured finite element method. Such wavefield simulations are suitable for GPUs, which have been improving their computational performance in recent years, and the use of GPUs is expected to speed up such simulations. However, there is still room for speedup and improving energy efficiency of such simulations using GPUs, since the performance of GPUs is not fully exploited just by its simple use, and the conventional method involves some numerical dispersion. In this paper, we propose a method for fast and efficient explicit structured-mesh wavefield simulation on GPUs by utilizing INT8 Tensor Cores and reducing numerical dispersion. We implemented the proposed method on GPUs and evaluated its performance in detail using an application example that simulates a real problem, and showed that it is faster and more efficient than conventional methods on many-node CPU-based systems and multiple GPU-based systems. This paper is the extended version of Ichimura et al. (2024).<span><span>[1]</span></span></div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102659"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TECNN: Identification of key nodes in complex networks based on transformer encoder and Convolutional Neural Network 基于变压器编码器和卷积神经网络的复杂网络关键节点识别

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-28 DOI: 10.1016/j.jocs.2025.102632

Lihui Sun, Pengli Lu

{"title":"TECNN: Identification of key nodes in complex networks based on transformer encoder and Convolutional Neural Network","authors":"Lihui Sun, Pengli Lu","doi":"10.1016/j.jocs.2025.102632","DOIUrl":"10.1016/j.jocs.2025.102632","url":null,"abstract":"<div><div>In complex networks, identifying key nodes is crucial for controlling information dissemination, optimizing resource allocation, and enhancing network robustness. Although many methods for identifying key nodes have been proposed, most deep learning-based approaches lack in-depth study of multi-hop neighbor relationships when constructing node features, often ignoring critical information and thus affecting identification accuracy. To address this issue, we propose a hybrid model based on the Transformer encoder and Convolutional Neural Network (<strong>TECNN</strong>) to better capture comprehensive information of nodes and predict their diffusion influence. Firstly, we use the neighborhood aggregation module to aggregate the 7-hop neighbor features of the nodes, obtaining a neighborhood matrix for the nodes. Next, the neighborhood matrix is fed into the Transformer encoder to capture the long-range dependencies between nodes, producing new node feature representations. These new node representations are then input into the Convolutional Neural Network, and the structural information of the nodes is further extracted through multilayer convolutional operations. Finally, a fully connected layer is used to predict the influence of the nodes. We perform comparative experiments by comparing the TECNN algorithm with four classical centrality algorithms and three state-of-the-art deep learning-based algorithms on 12 networks. The experimental results show that TECNN performs well in terms of ranking accuracy, discriminative ability, and top-10 node identification precision.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102632"},"PeriodicalIF":3.1,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A surrogate model for studying random field energy release rates in 2D brittle fractures 研究二维脆性裂缝随机场能释放率的替代模型

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-28 DOI: 10.1016/j.jocs.2025.102635

Luis Blanco-Cocom , Marcos A. Capistrán , Jaroslaw Knap , J. Andrés Christen

{"title":"A surrogate model for studying random field energy release rates in 2D brittle fractures","authors":"Luis Blanco-Cocom , Marcos A. Capistrán , Jaroslaw Knap , J. Andrés Christen","doi":"10.1016/j.jocs.2025.102635","DOIUrl":"10.1016/j.jocs.2025.102635","url":null,"abstract":"<div><div>This article proposes a weighted-variational model as an approximated surrogate model to lessen numerical complexity and lower the execution times of brittle fracture simulations. Consequently, Monte Carlo studies of brittle fractures become possible when energy release rates are modeled as a random field. In the weighed-variational model, we propose applying a Gaussian random field with a Matérn covariance function to simulate a non-homogeneous energy release rate (<span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>) of a material. Numerical solutions to the weighed-variational model, along with the more standard but computationally demanding hybrid phase-field models, are obtained using the FEniCS open-source software. The results have indicated that the weighted-variational model is a competitive surrogate model of the hybrid phase-field method to mimic brittle fractures in real structures. This method reduces execution times by 90%. We conducted a similar study and compared our results with an actual brittle fracture laboratory experiment. We present an example where a Monte Carlo study is carried out, modeling <span><math><msub><mrow><mi>G</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> as a Gaussian Process, obtaining a distribution of possible fractures, and load–displacement curves.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102635"},"PeriodicalIF":3.1,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Path planning of complex environment based on hyper view ant colony algorithm 基于超视场蚁群算法的复杂环境路径规划

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-26 DOI: 10.1016/j.jocs.2025.102658

Junqi Yang, Feiyang Liu, Hongwei Zhang

{"title":"Path planning of complex environment based on hyper view ant colony algorithm","authors":"Junqi Yang, Feiyang Liu, Hongwei Zhang","doi":"10.1016/j.jocs.2025.102658","DOIUrl":"10.1016/j.jocs.2025.102658","url":null,"abstract":"<div><div>A hyper view ant colony algorithm is developed to deal with the issues of sluggish convergence and poor search ability of traditional ant colony algorithms in complex environments. The concept of hyper view ant and view selection mechanism are first introduced in the enhanced ant colony algorithm. Dijkstra algorithm is used to determine the optimal path for the reachable node set of hyper view ants, where the state transition is accomplished using the designed pheromone calculation method. In addition, this paper creates an ant knowledge database and introduces it into the state transition type, which makes the information communication between ants more adequate. The knowledge database will be updated via historical path, and its value will be adaptively loaded. Then, an ant view atrophy mechanism is developed to balance the time efficiency of the proposed algorithm, and a pheromone compensation method is given to ensure the adsorption of algorithm to optimal path. Finally, by the experiments in various complex environments, the statistics of different performance parameters show that the results of the proposed algorithm in this paper are better than the ones of the existing algorithms including traditional ant colony algorithm.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102658"},"PeriodicalIF":3.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A pseudospectral method for time-fractional PDEs with shifted Chebyshev and Lagrange interpolating polynomials on overlapping decomposed domains 在重叠分解域上具有移位切比雪夫和拉格朗日插值多项式的时间分数阶偏微分方程的伪谱方法

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-25 DOI: 10.1016/j.jocs.2025.102654

Nancy Mukwevho , Olumuyiwa Otegbeye , Shina Daniel Oloniiju

{"title":"A pseudospectral method for time-fractional PDEs with shifted Chebyshev and Lagrange interpolating polynomials on overlapping decomposed domains","authors":"Nancy Mukwevho , Olumuyiwa Otegbeye , Shina Daniel Oloniiju","doi":"10.1016/j.jocs.2025.102654","DOIUrl":"10.1016/j.jocs.2025.102654","url":null,"abstract":"<div><div>Time-fractional partial differential equations (TFPDEs) are powerful mathematical tools for modeling a wide range of physical and biological phenomena that exhibit memory effects, anomalous diffusion and non-local behavior. These classes of equations are crucial in capturing dynamics where the influence of past states affects future evolution, making them essential in many areas of applied science, such as heat transfer, viscoelasticity and anomalous diffusion. This study proposes a pseudospectral method that combines the weighted sums of the Chebyshev and Lagrange polynomials to numerically approximate the solutions of TFPDEs. The spatial domain is partitioned into uniform, overlapping subdomains, where the solution in each subdomain is represented as a weighted sum of the Lagrange interpolating polynomials. On the other hand, the time domain is treated as a whole without decomposition, and the solution in the temporal dimension is expanded using the first-kind shifted Chebyshev polynomials. We validate the accuracy and performance of the method through a series of test cases, covering both linear and nonlinear TFPDEs in one and multiple spatial dimensions. These examples showcase the method’s capability to handle the computational challenges associated with TFPDEs and underline its potential for broader applications in problems involving fractional dynamics. Specifically, the proposed technique is applied to resolve TFPDE, which models heat transfer on a disk, a problem relevant to modeling heat conduction in circular plates and semiconductor wafers. A time-dependent Gaussian heat source concentrated in a specific region of the disk is introduced to accurately simulate practical thermal diffusion dynamics. The gradual increase of the source term over time offers a more realistic representation of the evolving thermal diffusion process.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102654"},"PeriodicalIF":3.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pell wavelet-optimization procedure for two classes of fractional partial differential equations with nonlocal boundary conditions 两类具有非局部边界条件的分数阶偏微分方程的Pell小波优化方法

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-25 DOI: 10.1016/j.jocs.2025.102655

Sedigheh Sabermahani , Parisa Rahimkhani , Yadollah Ordokhani

引用次数: 0