Journal of Computational Science最新文献

{"title":"Variational Bayes for analysis of masked data","authors":"Himanshu Rai ,&nbsp;Sanjeev K. Tomer","doi":"10.1016/j.jocs.2025.102690","DOIUrl":"10.1016/j.jocs.2025.102690","url":null,"abstract":"<div><div>Bayesian competing risks analysis in presence of masked data often leads to an intractable posterior, for which Markov chain Monte Carlo (MCMC) methods are frequently utilized to evaluate various estimators of interest. However, while analyzing several risks simultaneously, MCMC methods may consume substantial amount of computation time. This paper introduces Variational Bayes, a machine learning technique, as an efficient alternative to MCMC for Bayesian analysis of competing risk data. Variational Bayes demonstrates faster convergence than MCMC, particularly in the context of extensive competing risk datasets. We compare the performance of variational Bayes over Gibbs sampling with respect to the number of considered risks through a simulation study. Additionally, we apply the two methods to analyze a real data set of computer hard drives.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102690"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842213","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}

{"title":"TECNN: Identification of key nodes in complex networks based on transformer encoder and Convolutional Neural Network","authors":"Lihui Sun,&nbsp;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":"2025-10-01","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}

{"title":"Anomaly detection and root cause analysis using convolutional autoencoders: A real case study","authors":"Piero Danti ,&nbsp;Alessandro Innocenti ,&nbsp;Sascha Sandomier","doi":"10.1016/j.jocs.2025.102685","DOIUrl":"10.1016/j.jocs.2025.102685","url":null,"abstract":"<div><div>Anomaly detection is the process of identifying unusual patterns in data that may indicate a deviation from the expected norm. This paper proposes a semi-supervised deep learning solution to detect anomalies of a YANMAR energy device that produces heat and power utilizing an internal combustion engine supplied with natural gas. The main equipment of the analysis is a 20 <span><math><mrow><mi>k</mi><msub><mrow><mi>W</mi></mrow><mrow><mi>e</mi></mrow></msub></mrow></math></span> micro-cogeneration unit installed in the energy plant of a facility school. More in detail, the dataset considered in this work consists of 12 features temporally acquired every 15 min. The authors exploit a deep learning architecture, an autoencoder with 1-D convolutional layers to retain temporal correlations, trained to learn the normal behavior of the cogenerator and report unseen operations. In consideration of the fact that autoencoders tend to yield false positives, a Fast-Fourier-Transform-based technique has been applied to filter spurious detections and improve the algorithm’s robustness. As the last contribution, a naive methodology to address the root cause of the anomalies has been explained and its effectiveness has been proved in a real malfunctioning of the CHP.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102685"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739414","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}

{"title":"An improved K-means algorithm based on persistent homology","authors":"NingNing Peng,&nbsp;Shanjunshu Gao,&nbsp;Xingzi Yin,&nbsp;Xueyan Zhan","doi":"10.1016/j.jocs.2025.102680","DOIUrl":"10.1016/j.jocs.2025.102680","url":null,"abstract":"<div><div>The traditional K-means algorithm has several limitations, including sensitivity to initial center, unstable clustering results, local optimal clustering results, and a large number of iterations. In this paper, we propose an improved clustering algorithm called PH-K-means that utilizes the persistent homology to identify k clusters in the data set. The algorithm calculates the length of the longest Betti number to obtain k Betti numbers, which represent the k clusters respectively. The data is then output in k Betty numbers, and the average value of the data in each Betti number is used as the initialization center of k clusters. The algorithm iterates until the difference of the square sum of the errors in the adjacent two clusters is less than the threshold value. The PH-K-means algorithm is tested on seven common data sets, and the results show that it has high accuracy, stable clustering results, and requires fewer iterations than traditional K-means, K-means++, UK-means, and K-means algorithms.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102680"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771517","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}

{"title":"A computational approach to developing two-derivative ODE-solving formulations: γβI-(2+3)P method","authors":"Mehdi Babaei","doi":"10.1016/j.jocs.2025.102653","DOIUrl":"10.1016/j.jocs.2025.102653","url":null,"abstract":"<div><div>This paper presents the first set of two-derivative γβ formulations for time-integration of initial value (IV) ordinary differential equations (ODEs) in applied science. It belongs to the extended families of general linear methods (GLMs) and Runge-Kutta (RK) methods covering both linear and nonlinear ODEs. The present formulation is an advanced version of the basic form <span><math><mrow><mi>α</mi><mi>I</mi><mo>−</mo><mo>(</mo><mi>q</mi><mo>+</mo><mi>r</mi><mo>)</mo><mi>P</mi></mrow></math></span>, previously published by the author [1]. The key idea behind these formulations is the body decomposition of the RK methods and GLMs into two distinctive parts including interpolation and integration. This interesting idea has many advantages. First, it increases the flexibility of the formulation process. Second, each of these parts is supported by strong theorems in numerical analysis and can be developed independently through its own theories. In addition to these advantages, a knowledge-based approach, strengthened with swarm intelligence, is employed to formulate the integrator. Accordingly, a significant level of expertise is utilized in formulating the integrators. It leads to a series of interconnectivity relations between the weights of the integrators. These are known as weighting rules (WRs), which come in different types. The interpolators are obtained from approximation theory in which a polynomial is fitted to a given set of data. Consequently, a number of high-precision interpolators are developed to collaborate with the extended integrator. They approximate solution values at intermediate stages of the integration step, while the integrator bridges between the start and end points of the step. Working with interpolators has the advantages of generating solution values at all stages. It enables us to report the solution at more points rather than merely the mesh points. All the WRs, integrator, interpolators, and the ODE are systematically arranged in a specific order to construct the new algorithms of <span><math><mrow><mi>γ</mi><mi>β</mi><mi>I</mi><mo>−</mo><mo>(</mo><mi>q</mi><mo>+</mo><mi>r</mi><mo>)</mo><mi>P</mi></mrow></math></span>. Butcher tableaus are also provided for the new methods. Finally, they are carefully verified on several IVPs, including long-term and high-frequency problems. The obtained results demonstrate the practicality and efficiency of the formulations, and confirm that the collaboration of WRs, integrators, and interpolators performs exceptionally well.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102653"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739416","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}

{"title":"A surrogate model for studying random field energy release rates in 2D brittle fractures","authors":"Luis Blanco-Cocom ,&nbsp;Marcos A. Capistrán ,&nbsp;Jaroslaw Knap ,&nbsp;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":"2025-10-01","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}

{"title":"A computational analysis of traffic cluster dynamics using a percolation-based approach in urban road networks","authors":"Yongsung Kwon ,&nbsp;Minjin Lee ,&nbsp;Mi Jin Lee ,&nbsp;Seung-Woo Son","doi":"10.1016/j.jocs.2025.102675","DOIUrl":"10.1016/j.jocs.2025.102675","url":null,"abstract":"<div><div>Understanding the dynamics of traffic clusters is crucial for enhancing urban transportation systems, particularly in managing congestion and free-flow states. This study applies computational percolation theory to analyze the formation and growth of traffic clusters within urban road networks, using high-resolution taxi data from Chengdu, China. Presenting the road network as a time-dependent, weighted, directed graph, we identify distinct behaviors in traffic jam and free-flow clusters through the growth patterns of giant connected components (GCCs). A persistent gap between GCC size curves, especially during rush hours, highlights disparities driven by spatial traffic correlations. These are quantified through long-range weight-weight correlations, offering a novel computational metric for traffic dynamics. Our approach demonstrates the influence of network topology and temporal variations on cluster formation, providing a robust framework for modeling complex traffic systems. The findings have practical implications for traffic management, including dynamic signal optimization, infrastructure prioritization, and strategies to mitigate congestion. By integrating graph theory, percolation analysis, and traffic modeling, this study advances computational methods in urban traffic analysis and offers a foundation for optimizing large-scale transportation systems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102675"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711667","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}

{"title":"A demonstration on the construction of modular neural network using elevator system that operates based on reinforcement learning","authors":"Ning Ning Chung ,&nbsp;Hamed Taghavian ,&nbsp;Mikael Johansson ,&nbsp;Lock Yue Chew","doi":"10.1016/j.jocs.2025.102678","DOIUrl":"10.1016/j.jocs.2025.102678","url":null,"abstract":"<div><div>We study how neural networks can perform the task of elevator dispatching of commuters from their origins to their destinations. Instead of applying a neural network in the conventional way, we construct a specific neural network architecture that optimizes the commuters’ traveling time after taking into account the domain knowledge and the efficacy of potential future actions. The constructed architecture is modular with building blocks of neuronal structure that serve specified functional roles. By relaxing the weights and then training this network via reinforcement learning, we show that it outperforms an agent that implements the standard elevator algorithm. More remarkably, we observe the spontaneous emergence of functional modules within the structure of the network in consequence of the action sequences experienced during training. This behavioral feature of the neural network makes it less of a black box, with specific aspects of its functions being explicitly discernible from its network connections.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102678"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724832","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}

{"title":"Corrosion-induced multiscale damage behavior of ultrahigh strength steel: An integrated simulation and experiment study","authors":"Weizheng Lu ,&nbsp;Zouxueyin Wang ,&nbsp;Libo Yu ,&nbsp;Shaohua Xing ,&nbsp;Andong Wang ,&nbsp;Yong Zhang ,&nbsp;Jia Li ,&nbsp;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":"2025-10-01","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}

{"title":"The variational multiscale element free Galerkin method for three-dimensional steady magnetohydrodynamics duct flows","authors":"Xiaohua Zhang ,&nbsp;Yujie Fan","doi":"10.1016/j.jocs.2025.102683","DOIUrl":"10.1016/j.jocs.2025.102683","url":null,"abstract":"<div><div>Magnetohydrodynamics (MHD) has extensive applications in diverse fields, making the study of three-dimensional (3D) MHD problems crucial. For MHD flows, when the Hartmann (<span><math><mrow><mi>H</mi><mi>a</mi></mrow></math></span>) number is large, leading to a convection-dominated regime where convection terms overcome diffusion. In such scenarios, standard Galerkin methods fail to suppress non-physical oscillations in solutions, as they lack inherent stabilization mechanisms for strong convection. This paper introduces the variational multiscale element free Galerkin (VMEFG) method to solve 3D steady MHD equations. The VMEFG method inherits the advantage of the element free Galerkin (EFG) method in avoiding the complex meshing process, which is particularly challenging for complex 3D problems. Moreover, compared with the EFG method, it shows enhanced stability in dealing with convection-dominant problems and can automatically generate stabilized parameters, outperforming other stabilization techniques. To verify the numerical stability and accuracy of the VMEFG method, several numerical experiments on various domains including cubic, annular cubic, spherical, and annular spherical domains were conducted and compared with EFG solutions and existing literature results. The results indicate that the VMEFG method can effectively avoid numerical oscillations and maintain stability for 3D MHD problems at high <span><math><mrow><mi>H</mi><mi>a</mi></mrow></math></span> number, providing a reliable and efficient solution for such problems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102683"},"PeriodicalIF":3.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713416","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}