Journal of Computational Science最新文献_第3页

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2025-12-26 DOI: 10.1016/j.jocs.2025.102777

José Miguel Ramírez-Sanz, David Martínez-Acha, Álvar Arnaiz-González, César García-Osorio, Juan J. Rodríguez

引用次数: 0

MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications MOOSE ProbML：用于计算能量应用的并行概率机器学习和不确定性量化

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2025-12-22 DOI: 10.1016/j.jocs.2025.102776

Somayajulu L.N. Dhulipala , Peter German , Yifeng Che , Zachary M. Prince , Xianjian Xie , Pierre-Clément A. Simon , Vincent M. Labouré , Hao Yan

{"title":"MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications","authors":"Somayajulu L.N. Dhulipala , Peter German , Yifeng Che , Zachary M. Prince , Xianjian Xie , Pierre-Clément A. Simon , Vincent M. Labouré , Hao Yan","doi":"10.1016/j.jocs.2025.102776","DOIUrl":"10.1016/j.jocs.2025.102776","url":null,"abstract":"<div><div>This paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — <span>Sampler</span>, <span>MultiApp</span>, <span>Reporter</span>, and <span>Surrogate</span> — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"94 ","pages":"Article 102776"},"PeriodicalIF":3.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884602","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 novel exact solution of extended single particle model for lithium-ion cell and its application to high capacity NMC battery 锂离子电池扩展单粒子模型的精确解及其在大容量NMC电池中的应用

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jocs.2025.102780

Arif Ullah Khan , Mahvish Samar , Arif Hussain , Mei Li , Wenting Xu , Zhiyu Mao , Zhongwei Chen

{"title":"A novel exact solution of extended single particle model for lithium-ion cell and its application to high capacity NMC battery","authors":"Arif Ullah Khan , Mahvish Samar , Arif Hussain , Mei Li , Wenting Xu , Zhiyu Mao , Zhongwei Chen","doi":"10.1016/j.jocs.2025.102780","DOIUrl":"10.1016/j.jocs.2025.102780","url":null,"abstract":"<div><div>In this work, the exact analytical solution of the reduced order model, developed by incorporating polynomial based electrolyte dynamics expressions in single particle model, is computed. The major purpose of this work is to propose the solution of governing equations which address the issue of computational cost but retain the accuracy of simulation results. Using a volume-average technique and polynomial approximations, the solid-phase diffusion governing equations are simplified from partial differential equations (PDEs) to linear ordinary differential equations (ODEs). A traditional method is subsequently employed to obtain precise analytical solutions for these ordinary differential equations (ODEs). After that, we can find the surface concentrations analytically by plugging the ODE solutions into their defining equations. In addition, the electrolyte concentrations and potential in all three regions are approximated with the analytical expressions. A comparison is established between extended single particle model (eSPM) simulations and full-order Pseudo 2-dimensional (P2D) model (simulated in PyBAMM) to access accuracy and consistency of the adopted procedure. The results show that absolute relative error in eSPM simulations is about 1 %-3 % up to C-rates = 2. Finally, the proposed exact solution is utilized to simulate the behavior of NMC battery.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"94 ","pages":"Article 102780"},"PeriodicalIF":3.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925534","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

Efficient utilization of streaming multiprocessors for the implementation of particle filter on graphics processing unit 高效利用流多处理器在图形处理单元上实现粒子滤波

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2025-12-24 DOI: 10.1016/j.jocs.2025.102778

Özcan Dülger

{"title":"Efficient utilization of streaming multiprocessors for the implementation of particle filter on graphics processing unit","authors":"Özcan Dülger","doi":"10.1016/j.jocs.2025.102778","DOIUrl":"10.1016/j.jocs.2025.102778","url":null,"abstract":"<div><div>The particle filter is a serial Monte Carlo estimation method. It is used in tracking applications in which the system or measurement model is highly nonlinear. The quality of the estimation improves as the number of particles increases; however, the computational cost also rises. The graphics processing units (GPUs) offer a promising solution for the particle filter by providing many cores in their architectures. To implement the particle filter on the GPU, we use CUDA as the parallel computing platform. The architecture of the GPU must be carefully considered when determining the parameters of CUDA kernels. Configuring the block size of CUDA kernels appropriately is essential for the efficient utilization of streaming multiprocessors (SMXs). In this study, we investigate the impact of block size on SMX efficiency, particularly in GPUs where the number of SMXs is not a power of two. We propose three distinct scenarios based on different block size configurations and provide a detailed discussion of the characteristics and resulting speedups of these scenarios. We conduct experiments on two different GPU boards, NVIDIA Tesla K20 and NVIDIA Tesla K40. In addition, we demonstrate a multi-GPU approach for the particle filter using these boards and discuss the associated challenges and resulting speedups in detail.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"94 ","pages":"Article 102778"},"PeriodicalIF":3.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840737","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

Algorithm for segmentation of multimodally distributed time series in accordance with their modes 多模态分布时间序列按模态分割算法

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2025-12-05 DOI: 10.1016/j.jocs.2025.102765

V.S. Petrakova, E.D. Karepova

{"title":"Algorithm for segmentation of multimodally distributed time series in accordance with their modes","authors":"V.S. Petrakova, E.D. Karepova","doi":"10.1016/j.jocs.2025.102765","DOIUrl":"10.1016/j.jocs.2025.102765","url":null,"abstract":"<div><div>The paper proposes an algorithm for dividing a time series with a multimodal distribution into long continuous segments corresponding to one of the modes of its distribution. We call such division of the series the Segmentation. The algorithm is a two-level classifier of time series elements: an element belongs to a segment, and a segment is assigned to a certain class. Each class is associated with a peak in the original histogram constructed for all elements of the series. We associate each histogram peak with a certain set of stable external conditions (operating modes) that affect the behavior of the observed variable. This refers us to the definition of non-stationarity of a series if this non-stationarity can be represented as a mixture of some distributions. The main idea for constructing the algorithm is to treat the elements of the series corresponding to one mode as a sample from a unimodal distribution. The two-level classifier takes into account the temporal nature of the series, i.e. the ordering of its elements in contrast to the sample. The algorithm operates under the assumption that the distribution of data in each resulting class is close to normal. The article proposes testing the algorithm on synthetic and real data.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"94 ","pages":"Article 102765"},"PeriodicalIF":3.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685904","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

Numerical method-informed DeepONet for refractivity inversion in waveguides 基于数值方法的波导折射率反演DeepONet

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2026-01-23 DOI: 10.1016/j.jocs.2026.102788

Mikhail S. Lytaev

引用次数: 0

ENOF:Outlier detection algorithm based on Elastic Neighborhood Outlier Factor ENOF：基于弹性邻域离群因子的离群点检测算法

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2025-12-13 DOI: 10.1016/j.jocs.2025.102766

Zhongping Zhang , Jinyu Dong , Kuo Wang , Zhongman Wang

{"title":"ENOF:Outlier detection algorithm based on Elastic Neighborhood Outlier Factor","authors":"Zhongping Zhang , Jinyu Dong , Kuo Wang , Zhongman Wang","doi":"10.1016/j.jocs.2025.102766","DOIUrl":"10.1016/j.jocs.2025.102766","url":null,"abstract":"<div><div>Outlier detection is one of the core problems in the field of data mining. To address the limitations of existing outlier detection algorithms, which are often sensitive to the nearest neighbor parameter <span><math><mi>k</mi></math></span>, struggle with complex data distributions, and demonstrate low accuracy in detecting various types of outliers, we propose a novel outlier detection algorithm based on the Elastic Neighborhood Outlier Factor (ENOF). This method accounts for neighborhood density variations across different samples and introduces the concept of Mutual Nearest Neighbors to determine the optimal value of <span><math><mi>k</mi></math></span> when a sample reaches a steady state. By doing so, the algorithm more comprehensively captures the neighborhood information of each data object. A global radius is defined to characterize the elastic neighborhood of each sample. Based on this, the concept of elastic neighborhood density is introduced to identify global outliers. For the remaining samples, a thresholding strategy is employed, and an elastic neighborhood outlier factor is formulated by incorporating the number of mutual neighbors, which facilitates the further identification of local outliers. The proposed algorithm has been experimentally validated on both synthetic and real datasets, and its effectiveness is demonstrated through comparisons with several classical and novel algorithms.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"94 ","pages":"Article 102766"},"PeriodicalIF":3.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790982","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

On qualitative uncertainty in modelling assumptions 关于建模假设中的定性不确定性

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.jocs.2025.102782

Derek Groen, Laura M. Harbach

引用次数: 0

Single/multi step optimal and modified homotopy perturbation method for strongly non-linear fractional initial value problems: Global series solution 强非线性分数阶初值问题的单/多步最优修正同伦摄动方法：全局级数解

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2026-01-16 DOI: 10.1016/j.jocs.2026.102786

Tapas Roy, Dilip K. Maiti

{"title":"Single/multi step optimal and modified homotopy perturbation method for strongly non-linear fractional initial value problems: Global series solution","authors":"Tapas Roy, Dilip K. Maiti","doi":"10.1016/j.jocs.2026.102786","DOIUrl":"10.1016/j.jocs.2026.102786","url":null,"abstract":"<div><div>The aim of this work is to explore the possibility for obtaining globally convergent series solutions for fractional order initial value problems (FIVPs) using our recently developed semi-analytical technique, known as the optimal and modified homotopy perturbation method (OM-HPM). First time here we demonstrate the phenomenon of global series solutions for both IVPs and FIVPs, providing guidelines for selecting the linear operator and initial guess when applying homotopy methods to fractional differential equations. One of our noble aims is to establish the necessary and sufficient conditions for the convergence of solutions for all contemporary homotopy-based methods, including OM-HPM, and to validate these conditions numerically. To illustrate the accuracy and efficiency of our technique, we apply it to six strongly nonlinear steady as well as chaotic fractional order IVPs, comparing our results with exact solutions and those obtained using numerical methods and other contemporary semi-analytical approaches. We exemplify the broader applicability of our single-step OM-HPM method in achieving global solutions for FIVPs, in contrast to contemporary homotopy-based multi-step methods. Additionally, we investigate the limitations of OM-HPM and introduce the multi-step OM-HPM, or MOM-HPM, specifically designed for chaotic solutions of the Lorenz system. We also provide recommendations on selecting the appropriate step size for all previously mentioned multi-step homotopy methods, aiming to minimize computational time within the designated domain. Our comprehensive theoretical and numerical results demonstrate that the single-step OM-HPM method is more advanced and effective than other existing single and multi-step methods for obtaining global series solutions for strongly nonlinear FIVPs.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"94 ","pages":"Article 102786"},"PeriodicalIF":3.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022641","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 joint graph neural network model incorporating rhetorical structure theory 结合修辞结构理论的联合图神经网络模型

IF 3.7 3区计算机科学

Journal of Computational Science Pub Date : 2026-02-01 Epub Date: 2026-01-14 DOI: 10.1016/j.jocs.2026.102784

Xiaoyang Wang , Wenfeng Liu , Xuesong Jiang , Jiangwei Wang , Yuzhen Yang , Yaling Gao , Longqing Bao

{"title":"A joint graph neural network model incorporating rhetorical structure theory","authors":"Xiaoyang Wang , Wenfeng Liu , Xuesong Jiang , Jiangwei Wang , Yuzhen Yang , Yaling Gao , Longqing Bao","doi":"10.1016/j.jocs.2026.102784","DOIUrl":"10.1016/j.jocs.2026.102784","url":null,"abstract":"<div><div>The rapid development of graph neural networks (GNNs) has led to significant advances in text classification. Current approaches primarily focus on converting text into graph representations by modeling word relationships, achieving promising results in natural language processing tasks. However, these methods often overlook crucial discourse-level information and the hierarchical organization of text documents. This paper introduces a novel framework that leverages Rhetorical Structure Theory (RST) to capture document-level discourse structure and proposes a multi-graph joint learning approach. Our main contributions are: (1) we propose the first framework to systematically integrate RST-based discourse structure with word-level features for neural text classification, (2) we develop methods to construct RST graphs that effectively preserve hierarchical discourse information, and (3) we design RSTGNN, a multi-graph joint learning architecture that combines discourse structure with semantic, syntactic, and sequential information through specialized attention mechanisms. Extensive experiments on five text classification datasets demonstrate that our approach achieves competitive performance with notable improvements on several benchmark datasets.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"94 ","pages":"Article 102784"},"PeriodicalIF":3.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022545","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