Journal of Computational Science最新文献

{"title":"Stochastic deep-Ritz for parametric uncertainty quantification","authors":"Ting Wang ,&nbsp;Jaroslaw Knap","doi":"10.1016/j.jocs.2025.102717","DOIUrl":"10.1016/j.jocs.2025.102717","url":null,"abstract":"<div><div>Scientific machine learning has become an increasingly important tool in materials science and engineering. It is particularly well suited to tackle material problems involving many variables or to allow rapid construction of surrogates of material models, to name just a few. Mathematically, many problems in materials science and engineering can be cast as variational problems. However, handling of uncertainty, ever present in materials, in the context of variational formulations remains challenging for scientific machine learning. In this article, we propose a deep-learning-based numerical method for solving variational problems under uncertainty. Our approach seamlessly combines deep-learning approximation with Monte Carlo sampling. The resulting numerical method is powerful yet remarkably simple. We assess its performance and accuracy on a number of variational problems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102717"},"PeriodicalIF":3.7,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118610","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 accurate and stable space-time radial basis function collocation method for transient coupled thermo-mechanical analysis","authors":"Xiaohan Jing ,&nbsp;Lin Qiu ,&nbsp;Hong Zhao ,&nbsp;Zeqian Zhang ,&nbsp;Yaoming Zhang ,&nbsp;Yan Gu","doi":"10.1016/j.jocs.2025.102720","DOIUrl":"10.1016/j.jocs.2025.102720","url":null,"abstract":"<div><div>In this study, an accurate and stable space-time radial basis function (STRBF) collocation method is developed to solve two- and three-dimensional dynamic coupled thermo-mechanical problems. The proposed method enhances numerical precision by strategically positioning source points beyond the computational domain through space-time scaling factors. To address the challenge of selecting the optimal shape parameter, a new coupled STRBF is formulated by combining the Multiquadric function with the conical spline. Furthermore, a multiscale computational strategy is implemented to mitigate numerical instability in the resulting linear system. The effectiveness of the developed approach is demonstrated through four numerical examples involving complex geometries and different initial and boundary conditions. Numerical results show that, compared to the traditional RBF collocation method, the developed scheme not only enhances computational accuracy but also significantly reduces the dependence on the choice of shape parameter, making it a promising method for dealing with transient coupled thermo-mechanical problems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102720"},"PeriodicalIF":3.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158211","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":"Heuristic Custom Similarity Index (HCSI): A novel machine learning approach for link prediction","authors":"Paraskevas Dimitriou,&nbsp;Vasileios Karyotis","doi":"10.1016/j.jocs.2025.102719","DOIUrl":"10.1016/j.jocs.2025.102719","url":null,"abstract":"<div><div>Link prediction is a fundamental task in network analysis, aiming at predicting missing or future connections between nodes in a network. With the growing availability of complex network data in fields like social networks, biological systems, the Internet, and scientific collaboration networks, accurate link prediction methods are becoming increasingly critical. Neighborhood or graph based link prediction algorithms are applied identically to different types of networks so that any differences in their structures are not exploited efficiently. Machine or deep learning based link prediction algorithms apply to each kind of network differently depending on the type of network, due to the unique characteristics of each domain, but frequently, most of them give poor results. In this paper, we propose a novel approach for link prediction, leveraging the power of machine learning and evolutionary algorithms. Our method utilizes local network information by encoding the network topology into link embeddings through a heuristic machine learning architecture. We introduce a novel tool to extract features from network structure effectively and combine them in an effective way through an evolutionary algorithm improving the discriminative power of link embeddings. We evaluate our method on eleven benchmark datasets and demonstrate its superior performance compared to a series (eleven in total) of effective and state-of-the-art algorithms. Our approach advances the state-of-the-art in link prediction yielding better results than other methods in all the networks we have applied it to.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102719"},"PeriodicalIF":3.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158210","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":"Numerical solution of the biological SIR model for COVID-19 with convergence analysis","authors":"Walid Remili ,&nbsp;Wen-Xiu Ma","doi":"10.1016/j.jocs.2025.102704","DOIUrl":"10.1016/j.jocs.2025.102704","url":null,"abstract":"<div><div>This study investigates the numerical solution of the biological Susceptible–Infectious–Recovered model for COVID-19 over extended time intervals using the shifted Chebyshev polynomial collocation method. Initially, the original problem is reformulated into a nonlinear Volterra integral equation for the susceptible population. The shifted Chebyshev polynomials are then employed to derive the numerical solution. A comprehensive convergence analysis of the collocation method is conducted to ensure the reliability and accuracy of the proposed approach. Finally, numerical simulations are performed for various parameter configurations that influence the system’s coefficients. Our method is compared with existing approaches, providing insights into the model’s dynamics under different conditions.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102704"},"PeriodicalIF":3.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095810","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":"Darcy-scale digital core models for rock properties upscaling and computational domain reduction","authors":"Denis Orlov,&nbsp;Batyrkhan Gainitdinov,&nbsp;Dmitry Koroteev","doi":"10.1016/j.jocs.2025.102715","DOIUrl":"10.1016/j.jocs.2025.102715","url":null,"abstract":"<div><div>The rapid development of Digital Rock Physics (DRP) requires the elaboration of robust techniques for closing the gaps between different scales of rock studies (upscaling). The upscaling workflows are especially needed to support the applicability of DRP for heterogeneous rocks. Basically, DRP involves two primary stages: model construction and simulation of physical processes on the models created. For heterogeneous rocks, there is an inherent trade-off between the spatial resolution of the data and the representativeness of the model size. The primary objective of this study was to implement and test a technique for upscaling digital core models from microscale to macroscale, enabling the computation of rock properties while accounting for heterogeneity of various scales. The upscaling is based on establishing correlations between tomography data of different resolutions and transforming low-resolution tomography into a multi-class model according to the defined correlation. The convolutional neural network for high-resolution tomography data was considered as the optimal algorithm for transforming low-resolution tomography into a multi-class model. The output of the neural network was an upscaled model of lower resolution than the original tomography image. Each cell in the upscaled model belonged to one of several types of formation, whose generalized characteristics were determined on the basis of the analysis of high-resolution tomography data. To validate the upscaling technique, we constructed a digital model of a complex carbonate reservoir based on data from multi-scale microtomography (<span><math><mi>μ</mi></math></span>CT). A Darcy-scale model has been used and validated as a multi-class model, enabling the computation of flows in pore samples of various scales. By incorporating diverse pore space structures as different classes in the Darcy-scale model, it is possible to preserve the substantial physical size of the model while enhancing its level of complexity.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102715"},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095811","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":"CKDTA: A chemical knowledge-enhanced framework for drug–target affinity prediction","authors":"Xingran Zhao ,&nbsp;Yanbu Guo ,&nbsp;Bingyi Wang ,&nbsp;Weihua Li","doi":"10.1016/j.jocs.2025.102706","DOIUrl":"10.1016/j.jocs.2025.102706","url":null,"abstract":"<div><div>Accurate drug–target affinity (DTA) prediction is a cornerstone of efficient drug discovery, as it directly accelerates the screening of potential therapeutic candidates, reduces the cost of preclinical experiments, and shortens the development cycle of new drugs. However, existing deep learning-based methods face two main challenges: (I) Purely data-driven approaches struggle to capture the functional semantics of molecules, such as the role of specific functional regions and chemical element properties in binding interactions, due to the lack of integration with chemical prior knowledge, leading to unreliable predictions; (II) the integration of topological structure from graphs and long-range dependencies from sequences is insufficient, often failing to capture complementary features, limiting the model’s generalization ability, especially for novel drugs or targets commonly encountered in early drug discovery . To address these issues, we propose <strong>CKDTA</strong>, a <strong>C</strong>hemical <strong>K</strong>nowledge Enhanced framework for <strong>D</strong>rug-<strong>T</strong>arget <strong>A</strong>ffinity prediction. Our framework introduces two key innovations: (1) a chemical knowledge-enhanced molecular modeling approach, which constructs a multi-layer molecular graph incorporating atom-level features, chemical element information, and functional regions, enabling the capture of functional semantics through a hierarchical attention mechanism, while leveraging chemical prior knowledge; (2) a co-attention module designed to optimize sequence interaction information by leveraging graph-based interaction data, compensating for the lack of spatial structural information in sequence data. This module fully exploits the topological structure of graphs and the long-range dependencies in sequences, capturing complementary features. Extensive experiments on benchmark datasets demonstrate that CKDTA outperforms state-of-the-art methods. Furthermore, cold-start experiments validate its generalizability, highlighting its potential for drug discovery applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102706"},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095812","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":"Approach to global path planning and optimization for mobile robots based on multi-local gravitational potential fields bias-P-RRT*","authors":"Leiwen Yuan ,&nbsp;Jingwen Luo","doi":"10.1016/j.jocs.2025.102718","DOIUrl":"10.1016/j.jocs.2025.102718","url":null,"abstract":"<div><div>The sampling-based method has strong environmental adaptability and probability completeness, providing an effective solution for mobile robot path planning. However, the conventional rapidly-exploring random trees (RRT) algorithm often presents slow convergence and inefficient search paths. In this sense, this paper proposes a mobile robot path planning and optimization algorithm based on P-RRT* that incorporates multi-local gravitational potential fields and bias sampling, i.e., multi-local gravitational potential fields Bias-P-RRT* (MLGPFB-P-RRT*). The algorithm adds a local gravitational field between the starting point and the target point to better guide the direction of random tree growth, and directly connects the center of the last local gravitational field to the target point to accelerate the convergence of the random tree at the target point. Meanwhile, the introduction of bias sampling based on local potential fields to optimize the generation quality of random points, thereby improving the generation position of new nodes and reducing the randomness of sampling for mobile robots in the workspace. Then, a collision detection method between sampling nodes and obstacles was developed, which can quickly determine the feasibility of the sampling path. Finally, the generated path is optimized and smoothed through pruning optimization and quadratic B-spline function. A series of simulation studies and mobile robot experiments demonstrate the superior performance of the proposed algorithm.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102718"},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095808","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":"Efficient numerical simulation of variable-order fractional diffusion processes with a memory kernel","authors":"Sabita Bera ,&nbsp;Mausumi Sen ,&nbsp;Sujit Nath","doi":"10.1016/j.jocs.2025.102705","DOIUrl":"10.1016/j.jocs.2025.102705","url":null,"abstract":"<div><div>Diffusion equations are fundamental in modeling the transport of heat, mass, or contaminants in porous media. However, classical models often fail to capture the anomalous diffusion behavior inherent in heterogeneous and memory-dependent materials. To address this, we investigate a fractional diffusion integro-differential equation involving variable-order derivatives in both time and space, subject to suitable conditions. The solutions are shown to exist and be unique through the rigorous application of fixed-point theorems. A finite difference-based numerical scheme is formulated to handle the variable-order fractional operators and convolution-type integral terms efficiently. Stability analysis confirms the accuracy and robustness of the method. In addition, approximate solutions are computed for three representative cases:(i) constant-order fractional diffusion (<span><math><mrow><mi>α</mi><mo>=</mo><mtext>constant</mtext></mrow></math></span>), (ii) time-dependent order <span><math><mrow><mi>α</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>, and (iii) fully variable-order <span><math><mrow><mi>α</mi><mrow><mo>(</mo><mi>x</mi><mo>,</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>. By incorporating variable order dynamics and integro-differential structures, this work extends conventional models and provides a unified framework for simulating complex transport processes in porous media.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102705"},"PeriodicalIF":3.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095809","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":"Numerical study of two-dimensional sediment transport using momentum-conserving staggered grid scheme","authors":"Riski Kurniawan ,&nbsp;Sri Redjeki Pudjaprasetya ,&nbsp;Rani Sulvianuri","doi":"10.1016/j.jocs.2025.102714","DOIUrl":"10.1016/j.jocs.2025.102714","url":null,"abstract":"<div><div>Sediment transport plays a crucial role in the evolution of bed morphology through deposition and erosion. This study presents numerical simulations of two-dimensional sediment transport induced by fluid flow. The fluid-sediment interaction is governed by a capacity model, i.e., the coupled system of shallow water and Exner equations, a simplification of more physically advanced non-capacity models. The system is solved using a momentum-conserving staggered grid (MCS) scheme. Model validation is performed using the Meyer-Peter and Müller (MPM) bedload transport formula, applied to experimental data from dam-break flows in various channel configurations. The proposed method successfully reproduces trends in the evolution of the water surface and quasi-steady sediment profiles. In general, the MCS scheme provides more accurate water level predictions than the numerical benchmark schemes. Although the predictions of maximum depths of deposition and erosion are less accurate, the overall results are consistent with those obtained from non-capacity models. Furthermore, the model is applied to the Kampar River estuary to simulate sediment transport due to the tidal bore.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102714"},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049295","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 study on pest control models based on nonlinear threshold control","authors":"Yongfeng Li ,&nbsp;Leyan Liang ,&nbsp;Zhong Zhao","doi":"10.1016/j.jocs.2025.102694","DOIUrl":"10.1016/j.jocs.2025.102694","url":null,"abstract":"<div><div>The pest number trigger threshold strategy has been widely used in the control of pests in agricultural production. In this study, pest populations are managed by using an integrated nonlinear threshold function and a saturation function. The existence conditions of various equilibrium points and sliding sections in the system are derived. Theoretical analysis and numerical simulation results show the existence of boundary equilibrium bifurcations, tangency bifurcations and limit cycle bifurcations caused by discontinuous boundary. It is worth noting that persistence and non-smooth folding can be observed in the boundary equilibrium bifurcations. At the same time, because the nonlinear threshold control strategy is adopted in this study, the change of the sliding section of the model is more complicated. The numerical simulation results show that if there is an unstable focus in the model, a sliding homoclinic cycle will appear with the occurrence of boundary saddle point bifurcation, and then form a crossing limit cycle. The sensitivity analysis results of the system show that if the threshold level is too low, the control measures do not achieve the desired results. Too high threshold selection will cause unnecessary economic losses. Therefore, our results show that an appropriate threshold should be set to reduce economic losses while ensuring that the number of pests is in a lower stable state.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102694"},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096008","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}