Communications in Nonlinear Science and Numerical Simulation最新文献_第10页

Conservation laws of mean field games equations 平均野战方程的守恒定律

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-07 DOI: 10.1016/j.cnsns.2025.108796

Roman Kozlov

引用次数: 0

On computation of Darboux polynomials for full Toda lattice 满Toda格的Darboux多项式的计算

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-07 DOI: 10.1016/j.cnsns.2025.108815

A.V. Tsiganov

引用次数: 0

Abnormal dynamics in cascading models for time-weighted path optimization 时间加权路径优化级联模型中的异常动力学

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-06 DOI: 10.1016/j.cnsns.2025.108823

Jianwei Wang, Yiyang Qian, Huize Hu

{"title":"Abnormal dynamics in cascading models for time-weighted path optimization","authors":"Jianwei Wang, Yiyang Qian, Huize Hu","doi":"10.1016/j.cnsns.2025.108823","DOIUrl":"10.1016/j.cnsns.2025.108823","url":null,"abstract":"<div><div>In real traffic networks, route choice preferences tend to prioritize the shortest travel time over the shortest distance. To address this, we propose a cascading failure model that incorporates time dynamics. By introducing the BPR function, our model quantifies edge transmission time, identifies time-optimal paths, and distributes loads proportionally based on edge time weights. Three parameters are introduced to redefine cascading dynamics and evaluate network robustness.Simulations on both artificial and real-world networks reveal several key findings: First, increasing node weights leads to an uneven load distribution, thereby weakening the network’s robustness. Second, the network exhibits both the “capacity paradox” and the “expansion paradox.” The capacity paradox indicates that increasing the capacity of edges can actually reduce network robustness. In networks with loop structures, enlarging edge capacities redirects traffic to critical edges, creating bottlenecks that amplify cascading failures. Similarly, the expansion paradox shows that increasing maximum capacity can destabilize the network by concentrating loads on key edges, which, if overloaded, can trigger widespread failures. These findings challenge the conventional assumptions that enhancing capacity universally improves robustness. Instead, our results emphasize the importance of balancing topological design and load distribution.For practical networks, strategies such as diversifying travel routes and limiting the load on hub nodes prove to be more effective than simply increasing capacity. This work advances cascading failure modeling by integrating time dynamics and provides actionable insights for improving infrastructure resilience.</div></div>","PeriodicalId":50658,"journal":{"name":"Communications in Nonlinear Science and Numerical Simulation","volume":"147 ","pages":"Article 108823"},"PeriodicalIF":3.4,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791652","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

Neurodynamic optimization algorithm for split equality problems and application to sparse signal recovery 分裂等式问题的神经动力学优化算法及其在稀疏信号恢复中的应用

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-06 DOI: 10.1016/j.cnsns.2025.108822

Binghao OuYang , Yong Wang , Xingxing Ju , Weichuang Yu

引用次数: 0

An efficient numerical scheme for two-dimensional nonlinear time fractional Schrödinger equation 二维非线性时间分数薛定谔方程的高效数值方案

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-05 DOI: 10.1016/j.cnsns.2025.108824

Jun Ma , Tao Sun , Hu Chen

引用次数: 0

LRBF-Based energy-conserving time splitting schemes for the 2D Maxwell equations 基于lrbf的二维Maxwell方程的节能时分裂方案

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-05 DOI: 10.1016/j.cnsns.2025.108813

Rong Gao , Jialin Hong , Linghua Kong , Qi Wu

引用次数: 0

A hybrid kernel-free boundary integral method with operator learning for solving parametric partial differential equations in irregular domains 求解不规则区域参数偏微分方程的算子学习混合无核边界积分方法

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-05 DOI: 10.1016/j.cnsns.2025.108816

Shuo Ling , Liwei Tan , Wenjun Ying

{"title":"A hybrid kernel-free boundary integral method with operator learning for solving parametric partial differential equations in irregular domains","authors":"Shuo Ling , Liwei Tan , Wenjun Ying","doi":"10.1016/j.cnsns.2025.108816","DOIUrl":"10.1016/j.cnsns.2025.108816","url":null,"abstract":"<div><div>The Kernel-Free Boundary Integral (KFBI) method presents an iterative solution to boundary integral equations arising from elliptic partial differential equations (PDEs). This method effectively addresses elliptic PDEs on irregular domains, including the modified Helmholtz, Stokes, and elasticity equations. The rapid evolution of neural networks and deep learning has invigorated the exploration of numerical PDEs. An increasing interest is observed in deep learning approaches that seamlessly integrate mathematical principles for investigating numerical PDEs. We propose a hybrid KFBI method, integrating the foundational principles of the KFBI method with the capabilities of deep learning. This approach, within the framework of boundary integral method, designs a network to approximate the solution operator for the corresponding integral equations by mapping the parameters, inhomogeneous terms and boundary information of PDEs to the boundary density functions, which can be regarded as solution of the integral equations. The models are trained using data generated by the Cartesian grid-based KFBI algorithm, exhibiting robust generalization capabilities. It accurately predicts density functions across diverse boundary conditions and parameters within the same class of equations. Experimental results demonstrate that the trained models can directly infer the boundary density function with satisfactory precision, obviating the need for iterative steps in solving boundary integral equations. Furthermore, applying the inference results of the models as initial values for iterations is also reasonable; this approach can retain the inherent second-order accuracy of the KFBI method while accelerating the traditional KFBI approach by reducing about 50% iterations across a range of problems.</div></div>","PeriodicalId":50658,"journal":{"name":"Communications in Nonlinear Science and Numerical Simulation","volume":"147 ","pages":"Article 108816"},"PeriodicalIF":3.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821183","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 parameter-robust and decoupled discretization scheme for nonlinear Biot’s model in poroelasticity 孔隙弹性非线性Biot模型的参数鲁棒解耦离散化方法

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-05 DOI: 10.1016/j.cnsns.2025.108798

Linshuang He , Xi Li , Minfu Feng

{"title":"A parameter-robust and decoupled discretization scheme for nonlinear Biot’s model in poroelasticity","authors":"Linshuang He , Xi Li , Minfu Feng","doi":"10.1016/j.cnsns.2025.108798","DOIUrl":"10.1016/j.cnsns.2025.108798","url":null,"abstract":"<div><div>We present a new robust and decoupled scheme for Biot’s model with displacement-dependent nonlinear permeability. The scheme combines a low-order H(div)-conforming element pair <span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>⊕</mo><msub><mrow><mi>RT</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>−</mo><msub><mrow><mi>P</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></math></span> approximation with a first-order semi-explicit time discretization. Two stabilization terms are incorporated into the modified conforming-like formulation to achieve the scheme’s stability. One term penalizes the nonconformity of the H(div)-conforming component, and the other stabilizes pressure oscillations. The constructed scheme sequentially solves the displacement and pressure, which naturally linearizes the nonlinear terms without additional internal iterations, thus enhancing the computational efficiency. We improve the weak coupling condition and then prove optimal convergence in space and time under this condition. Meanwhile, our scheme provides robust solutions that do not suffer from volumetric locking as the Lamé coefficient tends to infinity and spurious pressure oscillations as the constrained specific storage coefficient and the permeability or time step go to zero. Finally, we verify these theoretical results with several numerical examples of nonlinear displacement–permeability relationships.</div></div>","PeriodicalId":50658,"journal":{"name":"Communications in Nonlinear Science and Numerical Simulation","volume":"147 ","pages":"Article 108798"},"PeriodicalIF":3.4,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786227","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

Accelerated subgradient extragradient methods with increasing self-adaptive step size for variational inequalities 变分不等式自适应步长增大的加速次梯度外扩方法

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-04 DOI: 10.1016/j.cnsns.2025.108794

Zhongbing Xie , Min Li

引用次数: 0

Dual-triggered scheme for adaptive neural control of MIMO nonlinear switched systems against sensor and actuator attacks MIMO非线性切换系统抗传感器和执行器攻击的双触发自适应神经控制方案

IF 3.4 2区数学

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-04 DOI: 10.1016/j.cnsns.2025.108811

Zixiang Zhao , Liang Zhang , Shuyi Yang , Ning Zhao , Yongchao Liu

引用次数: 0