Computers & Mathematics with Applications最新文献_第3页

Spatiotemporal dynamics deduced by nonlocal delay competition in a diffusive Lotka-Volterra population model 扩散Lotka-Volterra种群模型的非局部延迟竞争时空动力学

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-22 DOI: 10.1016/j.camwa.2025.07.008

Xiaosong Tang , Jiaxin Shen , Xinchang Wang , Zhaoyun Zeng , Jingwen Zhu

{"title":"Spatiotemporal dynamics deduced by nonlocal delay competition in a diffusive Lotka-Volterra population model","authors":"Xiaosong Tang , Jiaxin Shen , Xinchang Wang , Zhaoyun Zeng , Jingwen Zhu","doi":"10.1016/j.camwa.2025.07.008","DOIUrl":"10.1016/j.camwa.2025.07.008","url":null,"abstract":"<div><div>In this article, under the influence of nonlocal delay competition, we are devoted to investigating spatiotemporal dynamics of a diffusive Lotka-Volterra population model. According to the different values of some parameters, the original model may be changed into Lotka-Volterra predator-prey model, cooperative population model, or competition population model. Then, through the characteristic equation analysis, we find that nonlocal delay competition can deduce the presence of Turing-Hopf bifurcation when the original model is Lotka-Volterra cooperative population model or competition population model. However, when the original model is Lotka-Volterra predator-prey model, nonlocal delay competition cannot deduce the presence of Turing-Hopf bifurcation, but delay can deduce stability switches and the presence of Hopf bifurcation. Moreover, in the known literatures, to our knowledge, reaction-diffusion model with nonlocal delay competition has been investigated more rarely, which implies that our results in this paper are new. Finally, by presenting some numerical calculations and simulations, we obtain the rich results of stable spatially homogeneous periodic solutions, spatially steady state solutions, spatially inhomogeneous periodic solutions and stability switches deduced by delay.</div></div>","PeriodicalId":55218,"journal":{"name":"Computers & Mathematics with Applications","volume":"196 ","pages":"Pages 172-182"},"PeriodicalIF":2.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-constrained deep kernel learning for inverse problems with noisy data 带噪声数据逆问题的物理约束深度核学习

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-21 DOI: 10.1016/j.camwa.2025.07.022

Zhenjie Tang, Li He

{"title":"Physics-constrained deep kernel learning for inverse problems with noisy data","authors":"Zhenjie Tang, Li He","doi":"10.1016/j.camwa.2025.07.022","DOIUrl":"10.1016/j.camwa.2025.07.022","url":null,"abstract":"<div><div>We propose a novel physics-constrained deep kernel learning (PCDKL) to estimate physical parameters and learn forward solutions for problems described by partial differential equations (PDEs) and noisy data. In this framework, a Gaussian Process (GP) with a deep kernel is constructed to model the forward solution. The posterior function samples from the GP serve as surrogates for the PDE solution. These GP posterior samples are constrained by two likelihoods: one to fit the noisy observations and the other to enforce conformity with the governing equation. To efficiently and effectively infer the deep kernel and physical parameters, we develop a stochastic estimation algorithm based on the evidence lower bound (ELBO), which serves as a posterior regularization objective function. The effectiveness of the proposed PCDKL is demonstrated through a systematic comparison with a Bayesian physics-informed neural network (B-PINN), a state-of-the-art method for solving inverse problems in PDEs with noisy observations. Our experiments show that PCDKL not only achieves forward solutions with informative uncertainty estimates comparable to B-PINN, but also yields accurate estimates of physical parameters. These results suggest that PCDKL has significant potential for uncertainty quantification in forward solutions and accurate physical parameter estimation, making it valuable for practical applications.</div></div>","PeriodicalId":55218,"journal":{"name":"Computers & Mathematics with Applications","volume":"196 ","pages":"Pages 135-150"},"PeriodicalIF":2.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient iterative decoupling method for thermo-poroelasticity based on a four-field formulation 基于四场公式的热孔弹性有效迭代解耦方法

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-21 DOI: 10.1016/j.camwa.2025.07.021

Cai Mingchao , Li Jingzhi , Li Ziliang , Liu Qiang

引用次数: 0

A family of even-order edge-oriented nonconforming finite elements in 2D with efficient locally conservative flux reconstruction 具有有效局部保守通量重建的二维偶阶边缘非协调有限元族

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-21 DOI: 10.1016/j.camwa.2025.07.024

Gwanghyun Jo , Hyeokjoo Park

引用次数: 0

Vertex-centered control-volume mimetic finite difference methods 以顶点为中心的控制-模拟体积有限差分方法

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-21 DOI: 10.1016/j.camwa.2025.07.018

Rainer Helmig , Martin Schneider , Ivan Yotov

{"title":"Vertex-centered control-volume mimetic finite difference methods","authors":"Rainer Helmig , Martin Schneider , Ivan Yotov","doi":"10.1016/j.camwa.2025.07.018","DOIUrl":"10.1016/j.camwa.2025.07.018","url":null,"abstract":"<div><div>We develop a new class of vertex-centered control-volume mimetic finite difference methods on polytopal meshes for second order elliptic equations. The schemes are based on a mixed velocity-pressure formulation. The pressure is constant on dual mesh control-volumes constructed around the primary mesh vertices. The normal velocity is constant on the faces of the control-volumes, resulting in local mass conservation over the control-volumes. We consider both symmetric velocity integration rules constructed over the control-volumes, as well as non-symmetric quadrature rules constructed over sub-volumes obtained by the intersection of primary and dual elements. The latter choice allows for explicit gradient construction and local multipoint flux elimination within the primary elements, resulting in a positive definite vertex-centered pressure system. On simplicial, quadrilateral or hexahedral meshes, these local flux methods are closely related, and in some cases equivalent, to the classical vertex-centered control-volume finite element methods based on piecewise polynomial finite element basis functions for the pressure. The mimetic finite difference framework is utilized to analyze the well posedness and accuracy of the proposed methods. We establish first order convergence for the pressure and the velocity in the discrete mimetic norms, as well as second order pressure superconvergence in the case of symmetric quadrature rules. A series of numerical experiments illustrates the convergence properties of the methods on problems with varying degree of anisotropy, heterogeneity, and grid complexity in two and three dimensions.</div></div>","PeriodicalId":55218,"journal":{"name":"Computers & Mathematics with Applications","volume":"196 ","pages":"Pages 104-126"},"PeriodicalIF":2.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A weak Galerkin finite element method for the two-dimensional Burgers' equation 二维Burgers方程的弱Galerkin有限元法

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-17 DOI: 10.1016/j.camwa.2025.07.010

Xiaoxiao Chen, Yanli Chen

引用次数: 0

Improving pseudo-time stepping convergence for CFD simulations with neural networks 利用神经网络改进CFD模拟的伪时间步进收敛性

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-16 DOI: 10.1016/j.camwa.2025.07.006

Anouk Zandbergen , Tycho van Noorden , Alexander Heinlein

{"title":"Improving pseudo-time stepping convergence for CFD simulations with neural networks","authors":"Anouk Zandbergen , Tycho van Noorden , Alexander Heinlein","doi":"10.1016/j.camwa.2025.07.006","DOIUrl":"10.1016/j.camwa.2025.07.006","url":null,"abstract":"<div><div>Computational fluid dynamics (CFD) simulations of viscous fluids described by the stationary Navier–Stokes equations are considered. Depending on the Reynolds number of the flow, the Navier–Stokes equations may exhibit a highly nonlinear behavior. The system of nonlinear equations resulting from the discretization of the Navier–Stokes equations can be solved using nonlinear iteration methods, such as Newton's method. However, fast quadratic convergence is typically only obtained in a local neighborhood of the solution, and for many configurations, the classical Newton iteration does not converge at all. In such cases, so-called globalization techniques may help to improve convergence.</div><div>In this paper, pseudo-time stepping (also known as pseudo-transient continuation) is employed in order to improve nonlinear convergence. The classical algorithm is enhanced by a neural network model that is trained to predict a local pseudo-time step. Generalization of the novel approach is facilitated by predicting the local pseudo-time step separately on each element using only local information on a patch of adjacent elements as input. Numerical results for standard benchmark problems, including flow over a backward facing step geometry and Couette flow, show the performance of the machine learning-enhanced globalization approach; as the software for the simulations, the CFD Module of COMSOL Multiphysics® is employed.</div></div>","PeriodicalId":55218,"journal":{"name":"Computers & Mathematics with Applications","volume":"196 ","pages":"Pages 64-83"},"PeriodicalIF":2.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoupled, efficient and structure-preserving numerical scheme for a non-isothermal phase field sintering model 非等温相场烧结模型的解耦、高效和保持结构的数值格式

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-16 DOI: 10.1016/j.camwa.2025.07.007

Jingjie Cheng , Qing Xia , Binhu Xia , Junseok Kim , Yibao Li

引用次数: 0

A preconditioned fast finite volume method for two-dimensional conservative space-fractional diffusion equations on non-uniform meshes 非均匀网格上二维保守空间-分数扩散方程的预条件快速有限体积法

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-14 DOI: 10.1016/j.camwa.2025.07.005

Yuan Xu , Siu-Long Lei , Hai-Wei Sun , Zhi-Wei Fang

引用次数: 0

Concurrency of the exponential integrators for the glioblastoma brain tumor simulations 胶质母细胞瘤脑肿瘤模拟的指数积分器的并发性

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2025-07-14 DOI: 10.1016/j.camwa.2025.07.003

Magdalena Pabisz , Askold Vilkha , Dominika Ciupek , Maciej Woźniak , Maciej Paszyński

{"title":"Concurrency of the exponential integrators for the glioblastoma brain tumor simulations","authors":"Magdalena Pabisz , Askold Vilkha , Dominika Ciupek , Maciej Woźniak , Maciej Paszyński","doi":"10.1016/j.camwa.2025.07.003","DOIUrl":"10.1016/j.camwa.2025.07.003","url":null,"abstract":"<div><div>We employ the trace theory as a tool to investigate the concurrency of the novel implementation of the exponential integrators method using the multinomial theorem. The exponential integrators code is applied for the glioblastoma brain tumor simulations. The paper introduces a solid theoretical background for trace theory-based concurrency analysis and uses an important example of brain tumor simulations to validate its applicability. We perform our simulations on available public MRI scan databases. The spatial discretization uses the finite difference method. The temporal discretization uses the exponential integrators method. We employ the trace theory to identify basic undividable tasks and their dependency relation. Based on the coloring of the task dependency graph, we localize sets of tasks that can be executed fully in parallel. From the results of the trace theory-based concurrency analysis, we designed an efficient MATLAB code parallelized for the GPGPU. We test the implementation by running 1000 time steps of the glioblastoma brain tumor simulation over the three-dimensional brain data with 194 x 248 x 256 pixels. We measure the scalability of the code, reaching 32 times speedup, using 128 x 128 x 128 computational mesh that can be computed on RTX 2080 Super GPU card equipped with 3072 processing cores and 8 GB of memory.</div></div>","PeriodicalId":55218,"journal":{"name":"Computers & Mathematics with Applications","volume":"196 ","pages":"Pages 13-29"},"PeriodicalIF":2.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0