Journal of Computational Science最新文献

{"title":"Solving the dopant diffusion dynamics with physics-informed neural networks","authors":"Sungyeop Lee ,&nbsp;Jisu Ryu ,&nbsp;Young-Gu Kim ,&nbsp;Dae Sin Kim ,&nbsp;Hiroo Koshimoto ,&nbsp;Jaeshin Park","doi":"10.1016/j.jocs.2025.102695","DOIUrl":"10.1016/j.jocs.2025.102695","url":null,"abstract":"<div><div>Simulation plays a crucial role in the semiconductor chip manufacturing. In particular, process simulation is primarily used to solve the dopant diffusion dynamics, which describes the temporal evolution of doping profiles during the thermal annealing process. The diffusion dynamics constitutes a multiscale problem, formulated as a set of coupled partial differential equations (PDEs) with respect to the concentration of dopants and point defects. In this paper, we demonstrate that Physics-Informed Neural Networks (PINNs) can accurately predict not only the evolution of the doping profile, but also the unknown physical parameters, specifically the diffusivities appearing as PDE coefficients. Furthermore, we propose a physics-informed calibration method, which performs PDE-constrained optimization by leveraging a pre-trained PINN model. We experimentally verify that this post-processing significantly improves the accuracy of coefficients fine-tuning. To the best of our knowledge, this is the first demonstration of an annealing simulation for the semiconductor diffusion process using a physics-informed machine learning approach. This framework is expected to enable more efficient calibration of simulation parameters based on measurement data.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102695"},"PeriodicalIF":3.7,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863514","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":"Helium focused ion beam damage in silicon: Physics-informed neural network modeling of helium bubble nucleation and early growth","authors":"Shupeng Gao ,&nbsp;Qi Li ,&nbsp;M.A. Gosalvez ,&nbsp;Xi Lin ,&nbsp;Yan Xing ,&nbsp;Zaifa Zhou ,&nbsp;Qianhuang Chen","doi":"10.1016/j.jocs.2025.102696","DOIUrl":"10.1016/j.jocs.2025.102696","url":null,"abstract":"<div><div>Currently, the time and cost required to obtain large datasets limit the application of data-driven machine learning in nanoscale manufacturing. Here, we focus on predicting the nanoscale damage induced by helium focused ion beams (He-FIBs) on silicon substrates. We briefly review the most relevant atomistic defects and the partial differential equations (PDEs), or rate equations, that describe the mutual creation and annihilation of the defects, eventually leading to the amorphization of the substrate and, the nucleation and early growth of helium bubbles. The novelty comes from the use of a physics-informed neural network (PINN) to simulate quantitatively the evolution of the bubbles, thus bypassing the dataset availability problem. As usual, the proposed PINN learns the underlying physics through the incorporation of the residuals of the PDEs and corresponding Initial Conditions (ICs) and Boundary Conditions (BCs) in the network’s loss function. Meanwhile, the system of PDEs poses some challenges to the PINN modeling strategy. We find that (i) hard constraints need to be imposed on the network output in order to satisfy both BCs and ICs, (ii) all the inputs and outputs of the PINN need to be cautiously normalized to ensure convergence during training, and (iii) customized weights need to be carefully applied to all the PDE loss terms in order to balance their contributions, thus improving the accuracy of the PINN predictions. Once trained, the network achieves good prediction accuracy over the entire space-time domain for various ion beam energies and doses. Comparisons are provided against previous experiments and traditional numerical simulations, which are also implemented in this study using the Finite Difference Method (FDM). While the L2 relative errors for all collocated points remain below 10%, the accuracy of the PINN decreases at lower beam energies and larger ion doses, due to the presence of higher numerical gradients.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102696"},"PeriodicalIF":3.7,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863513","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":"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":"2025-08-08","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":"BAHA: Binary artificial hummingbird algorithm for feature selection","authors":"Ali Hamdipour ,&nbsp;Abdolali Basiri ,&nbsp;Mostafa Zaare ,&nbsp;Seyedali Mirjalili","doi":"10.1016/j.jocs.2025.102686","DOIUrl":"10.1016/j.jocs.2025.102686","url":null,"abstract":"<div><div>Datasets classification accuracy depends on their features. The presence of irrelevant and redundant features in the dataset leads to the reduction of classification accuracy. Identifying and removing such features is the main purpose in feature selection, which is an important step in the data science lifecycle. The objective of the Wrapper feature selection method is to reduce the number of selected feature (NSF) while improving the classification accuracy by working on a set of features. The feature selection is a challenging and computationally expensive problem that falls under the NP-complete category, so it requires computationally cheap and efficient algorithm to solve it. The artificial hummingbird algorithm (AHA) is a biological inspired optimization technique that mimics the unique flight capabilities and intelligent foraging tactics of hummingbirds in nature. Since feature selection is inherently a binary problem. In this paper, the binary form of the AHA meta-heuristic algorithm is proposed to show that binarizing the AHA meta-heuristic algorithm improves its performance for solving feature selection problems. The proposed method is tested on a standard benchmark dataset and compared with four state-of-the-art feature selection algorithms: Automata-based improved equilibrium optimizer with U-shaped transfer function (AIEOU), Whale optimization approaches for wrapper feature selection (WOA-CM), Ring theory-based harmony search (RTHS), and Adaptive switching gray-whale optimizer (ASGW). The results show the effectiveness of the proposed algorithm in searching for optimal features subset. The source code for the algorithm being proposed is accessible to the public on <span><span>https://github.com/alihamdipour/baha</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102686"},"PeriodicalIF":3.7,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863515","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":"2025-07-30","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":"2025-07-29","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":"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":"2025-07-28","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}

{"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":"2025-07-26","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":"Data-driven enhancement of the Hastings–Powell model using sparse identification algorithm","authors":"Nitu Kumari,&nbsp;Anurag Singh","doi":"10.1016/j.jocs.2025.102682","DOIUrl":"10.1016/j.jocs.2025.102682","url":null,"abstract":"<div><div>A significant challenge in various fields of science and engineering is extracting governing equations from data. Prey-predator models are particularly complex due to their nonlinear behavior, making traditional analytical methods insufficient for accurately capturing their dynamics. In this study, we introduce a data-driven approach to model the intricate dynamics of Hastings–Powell model solely from time series data. This article explores the application of the sparse identification of nonlinear dynamics (SINDy) and its extension, the SINDy-PI (parallel, implicit) method, in a model representing a chaotic food chain. The main goal is to determine the governing equations that describe the chaotic dynamics of the prey-predator populations. Hence, this study uses the parameters wherein the dynamics exhibit chaotic behavior. The method of SINDy was developed with the aim of identifying governing equations of nonlinear dynamical systems. In both methods, a library of potential terms are created and then a regression problem is solved. We have employed both methods as our model incorporates not only nonlinear terms but also rational terms. Our results shows that SINDy method is unable to find the exact form of governing equations but SINDy-PI method has the capability to accurately capture the authentic structure of the governing equations. In addition, we applied model selection techniques to identify the most parsimonious model possible. Through the application of SINDy and SINDy-PI, this research contributes to the advancement of data-centric approaches in ecological modeling, offering insights into the intricate dynamics of multi-species interactions within ecosystems. Further, for this study to be more realistic, utilizing real-world data from three-species would have been ideal. However, due to non-availability of three species real data, simulated data set has been used for validation purpose.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102682"},"PeriodicalIF":3.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724831","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 h-adaptive collocation method for Physics-Informed Neural Networks","authors":"Jan Trynda ,&nbsp;Paweł Maczuga ,&nbsp;Albert Oliver-Serra ,&nbsp;Luis Emilio García-Castillo ,&nbsp;Robert Schaefer ,&nbsp;Maciej Woźniak","doi":"10.1016/j.jocs.2025.102684","DOIUrl":"10.1016/j.jocs.2025.102684","url":null,"abstract":"<div><div>Despite their flexibility and success in solving partial differential equations, Physics-Informed Neural Networks (PINNs) often suffer from convergence issues, even failing to converge, particularly in problems with steep gradients or localized features. Several remedies have been suggested to solve this problem, but one of the most promising is the dynamical adaptation of the collocation points. This paper explores a novel adaptive sampling method, of a stochastic nature, based on the Adaptive Mesh Refinement used in the Finite Element Method. The error estimates in our refinement algorithm are based on the value of the residual loss function. We tested our method against a variety of 1D and 2D benchmark problems that exhibit steep gradients near certain boundaries, with promising results.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102684"},"PeriodicalIF":3.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722460","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}