Journal of Computational Science最新文献_第4页

DP-PINN+: A Dual-Phase PINN learning with automated phase division DP-PINN+：具有自动相位划分的双相PINN学习

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-06 DOI: 10.1016/j.jocs.2025.102637

Da Yan, Ligang He

{"title":"DP-PINN+: A Dual-Phase PINN learning with automated phase division","authors":"Da Yan, Ligang He","doi":"10.1016/j.jocs.2025.102637","DOIUrl":"10.1016/j.jocs.2025.102637","url":null,"abstract":"<div><div>Physics-Informed Neural Networks (PINNs) are a promising application of deep neural networks for the numerical solution of nonlinear partial differential equations (PDEs). However, it has been observed that standard PINNs may not be able to accurately fit all types of PDEs, leading to poor predictions for specific regions in the domain. A common solution is to partition the domain by time and train each time interval separately. However, this approach leads to the prediction errors being accumulated over time, which is especially the case when solving “stiff” PDEs. To address these issues, we propose a new PINN training scheme, called DP-PINN+ (Dual-Phase PINN+). DP-PINN+ divides the training into two phases based on a carefully chosen time point <span><math><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span>. The phase-1 training aims to generate the accurate solution at <span><math><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span>, which will serve as the additional intermediate condition for the phase-2 training. The method for determining the optimized value of <span><math><msub><mrow><mi>t</mi></mrow><mrow><mi>s</mi></mrow></msub></math></span> is proposed in this paper. Further,new sampling strategies are proposed to enhance the training process. These design considerations improve the prediction accuracy significantly. We have conducted the experiments to evaluate DP-PINN+ with both “stiff” and non-stiff PDEs, including 1D Burger’s Equation, 1D Allen–Cahn Equation, 2D and 3D Navier–Stokes Equations (i.e., 2D cylinder wake and 3D unsteady Beltrami flow). We compared DP-PINN+ with the state-of-the-art PINNs in literature, including Time Adaptive PINN, SA-PINN, bc-PINN and NSFNets.The results show that the solutions predicted by DP-PINN+ exhibit significantly higher accuracy. This paper is extended from our conference paper published in ICCS2024 Yan and He (2024) <span><span>[1]</span></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102637"},"PeriodicalIF":3.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322595","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

Unconditionally stable method for the high-order Allen–Cahn equation 高阶Allen-Cahn方程的无条件稳定方法

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-06 DOI: 10.1016/j.jocs.2025.102636

Seungyoon Kang, Youngjin Hwang, Junseok Kim

{"title":"Unconditionally stable method for the high-order Allen–Cahn equation","authors":"Seungyoon Kang, Youngjin Hwang, Junseok Kim","doi":"10.1016/j.jocs.2025.102636","DOIUrl":"10.1016/j.jocs.2025.102636","url":null,"abstract":"<div><div>We propose an unconditionally stable algorithm for the Allen–Cahn (AC) equation that incorporates a high-order free energy. The high-order AC equation improves the preservation of interfacial dynamics and suppresses noise. The proposed method guarantees unconditional stability, which is essential for precise phase transition modeling and preserving detailed characteristics. To effectively solve the governing equation, it is divided into two subproblems, each of which is solved separately. The nonlinear operator is handled using a frozen coefficient method, followed by a closed-form solution. The linear operator is solved by applying the discrete cosine transform. To verify the effectiveness of the proposed algorithm, we carried out various computational simulations in two- and three-dimensional space. The proposed method ensures unconditional stability, and therefore allows stable solutions even with relatively large time steps. Moreover, we investigate the notable characteristics of the high-order AC equation, particularly its enhanced capability to effectively handle phase separation phenomena in the presence of significant noise and complex phase interfaces.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102636"},"PeriodicalIF":3.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241866","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

Physics-informed neural networks for compliant robotic manipulators dynamic modeling 柔性机械臂动力学建模的物理信息神经网络

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-05 DOI: 10.1016/j.jocs.2025.102633

Zhiming Li , Shuangshuang Wu , Wenbai Chen , Fuchun Sun

引用次数: 0

Convolutional neural networks can diagnose schizophrenia 卷积神经网络可以诊断精神分裂症

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-04 DOI: 10.1016/j.jocs.2025.102634

Murside Degirmenci , Murat Surucu , Matjaž Perc , Yalcin Isler

引用次数: 0

A MATLAB code for finding the kernel of a simple polygon 一个寻找简单多边形核的MATLAB代码

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-02 DOI: 10.1016/j.jocs.2025.102616

Annamaria Mazzia

引用次数: 0

Enhanced metabolite-disease associations prediction via Neighborhood Aggregation Graph Transformer with Kolmogorov–Arnold Networks 基于Kolmogorov-Arnold网络的邻域聚集图转换器增强代谢物疾病关联预测

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-02 DOI: 10.1016/j.jocs.2025.102629

Pengli Lu , Jian Zhang , Wenzhi Liu , Fentang Gao

{"title":"Enhanced metabolite-disease associations prediction via Neighborhood Aggregation Graph Transformer with Kolmogorov–Arnold Networks","authors":"Pengli Lu , Jian Zhang , Wenzhi Liu , Fentang Gao","doi":"10.1016/j.jocs.2025.102629","DOIUrl":"10.1016/j.jocs.2025.102629","url":null,"abstract":"<div><div>Metabolites are essential products of cellular chemical reactions, critical for sustaining life and reproduction. Research shows that metabolite concentrations in patients differ from those in healthy individuals, making metabolite-based disease prediction crucial for diagnosis and treatment. To address the limitations of current computational methods in accuracy and interpretability, we propose a novel Neighborhood Aggregation Graph Transformer method (AGKphormer). This method enhances link relationships by optimizing the minimum nuclear norm using the Alternating Direction Method of Multipliers (ADMM) and incorporates Fast Kolmogorov–Arnold Networks (FastKAN) to improve both accuracy and interpretability. We first construct a heterogeneous network based on the correlation and similarity between metabolites and diseases. Then, we utilize the ADMM algorithm to enhance link relationships by solving the minimum nuclear norm, reducing sparse relationships between nodes and providing richer features for neural network learning. For the features learned by the graph convolutional network (GCN), we employ a Graph Transformer augmented with FastKAN to learn long-range dependencies. This approach enables global feature embedding and addresses GCN’s smoothness issue while enhancing interpretability. Through five-fold cross-validation, AGKphormer achieved average AUC and AUPR values of 97.32% and 97.34%, respectively, outperforming most methods and demonstrating its effectiveness in predicting disease-related metabolites. Additionally, case studies further confirm that AGKphormer is a reliable tool for discovering potential metabolites.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102629"},"PeriodicalIF":3.1,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241865","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 Ritz method with Fourier feature mapping: A deep learning approach for solving variational models of microstructure 具有傅里叶特征映射的深度里兹方法：一种解决微观结构变分模型的深度学习方法

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-06-02 DOI: 10.1016/j.jocs.2025.102631

Ensela Mema , Ting Wang , Jaroslaw Knap

引用次数: 0

Data assimilation by cellular neural network applied to Lorenz-63 system 细胞神经网络数据同化在Lorenz-63系统中的应用

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-05-30 DOI: 10.1016/j.jocs.2025.102587

César Magno Leite de Oliveira Júnior , Antonio Mauro Saraiva , Alexandre Cláudio Botazzo Delbem , Haroldo Fraga de Campos Velho , Gerônimo Gallarreta Zubiaurre Lemos , Fabrício Pereira Härter

引用次数: 0

Adaptive time-varying output formation tracking control for multi-agent system with dynamic event-triggered strategies 具有动态事件触发策略的多智能体系统自适应时变输出队列跟踪控制

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-05-28 DOI: 10.1016/j.jocs.2025.102630

Jie Wu , Xiaoyi Zhang , Xisheng Zhan , Tao Han , Huaicheng Yan

引用次数: 0

AGDFCDA: Adaptive graph convolutional network and dual feature for circRNA-disease association prediction AGDFCDA：用于circrna -疾病关联预测的自适应图卷积网络和双特征

IF 3.1 3区计算机科学

Journal of Computational Science Pub Date : 2025-05-28 DOI: 10.1016/j.jocs.2025.102615

Yan Zhao , Xin He , Junliang Shang , Daohui Ge , Jin-Xing Liu

{"title":"AGDFCDA: Adaptive graph convolutional network and dual feature for circRNA-disease association prediction","authors":"Yan Zhao , Xin He , Junliang Shang , Daohui Ge , Jin-Xing Liu","doi":"10.1016/j.jocs.2025.102615","DOIUrl":"10.1016/j.jocs.2025.102615","url":null,"abstract":"<div><div>Circular RNA (circRNA) is a special type of RNA molecule whose structure presents as a closed loop. Numerous studies have demonstrated that abnormal expression of circRNA is closely associated with the development of diverse diseases. Accurately predicting the association between the circRNA and disease is important for understanding the pathogenesis of disease and discovering potential biomarkers. However, the high cost and complexity of traditional biological experiments limit the development of research. By constructing computational models and performing bioinformatics analysis, it is possible to identify disease-related circRNA more efficiently and reveal its potential mechanism. This paper presents AGDFCDA, a computational model for circRNA-disease association prediction, featuring a dual feature extraction strategy. On the one hand, the strategy applies the fully connected neural network to reduce the redundant information in the initial features, while the hidden information of circRNA and disease is preliminarily extracted. On the other hand, the strategy introduces adaptive graph convolutional network to learn more comprehensive representation of circRNA and disease to realize further extraction of features. AGDFCDA is assessed using five-fold cross-validation, and the results indicate that it outperforms the comparison methods in predicting circRNA-disease associations. In addition, the results of case studies can provide reliable candidate circRNA for wet experiments to be carried out with effective cost savings.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102615"},"PeriodicalIF":3.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213482","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