Physica D: Nonlinear Phenomena最新文献_第6页

Phase-space quantum distorted stability pattern for Aubry–André–Harper dynamics aubry - andr<s:1> - harper动力学的相空间量子畸变稳定性模式

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-06 DOI: 10.1016/j.physd.2025.134700

A.E. Bernardini , O. Bertolami

引用次数: 0

Extraction and reconstruction of variable-coefficient governing equations using Res-KAN integrating sparse regression 基于Res-KAN积分稀疏回归的变系数控制方程提取与重构

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-06 DOI: 10.1016/j.physd.2025.134689

Ming-Hui Guo , Xing Lü , Yu-Xi Jin

{"title":"Extraction and reconstruction of variable-coefficient governing equations using Res-KAN integrating sparse regression","authors":"Ming-Hui Guo , Xing Lü , Yu-Xi Jin","doi":"10.1016/j.physd.2025.134689","DOIUrl":"10.1016/j.physd.2025.134689","url":null,"abstract":"<div><div>Extracting the governing equations for complex systems plays a crucial role in scientific discovery and engineering applications. Previous research often focus on static properties of governing equations, while real-world dynamics involve complex, evolving factors that influence the system behavior. This work proposes a novel approach that integrates the single-layer Kolmogorov-Arnold networks (KANs) in the downstream operations of physics-informed neural networks (PINNs), combined with an alternating training strategy using sparse regression algorithms. Different from the traditional methods, this approach relies solely on sparse data, without any prior knowledge, to reconstruct precise form of governing equations and simultaneously identify the variable-coefficient functions depending on single variables. By symbolizing the spline functions in the KAN layer, it can also derive the exact expressions of these coefficient functions and reveal the key parameters of real physical significance. Furthermore, when extending the framework to high-dimensional problems, KAN’s regularization enables weight sparsity enforcement, which removes redundant neurons and optimizes the network. Experiments on various benchmark problems demonstrate the robustness of our method to varying levels of data sparsity and noise, offering a new solution to the reconstruction and analysis of the governing equations.</div></div>","PeriodicalId":20050,"journal":{"name":"Physica D: Nonlinear Phenomena","volume":"481 ","pages":"Article 134689"},"PeriodicalIF":2.7,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Influence network reconstruction from discrete time-series of count data modelled by multidimensional Hawkes processes 影响网络重建从离散时间序列的计数数据建模的多维霍克斯过程

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-05 DOI: 10.1016/j.physd.2025.134705

Naratip Santitissadeekorn , Martin Short , David J.B. Lloyd

{"title":"Influence network reconstruction from discrete time-series of count data modelled by multidimensional Hawkes processes","authors":"Naratip Santitissadeekorn , Martin Short , David J.B. Lloyd","doi":"10.1016/j.physd.2025.134705","DOIUrl":"10.1016/j.physd.2025.134705","url":null,"abstract":"<div><div>Identifying key influencers from time series data without a known prior network structure is a challenging problem in various applications, from crime analysis to social media. While much work has focused on event-based time series (timestamp) data, fewer methods address count data, where event counts are recorded in fixed intervals. We develop network inference methods for both batched and sequential count data. Here the strong network connection represents the key influences among the nodes. We introduce an ensemble-based algorithm, rooted in the expectation–maximization (EM) framework, and demonstrate its utility to identify node dynamics and connections through a discrete-time Cox or Hawkes process. For the linear multidimensional Hawkes model, we employ a minimization–majorization (MM) approach, allowing for parallelized inference of networks. For sequential inference, we use a second-order approximation of the Bayesian inference problem. Under certain assumptions, a rank-1 update for the covariance matrix reduces computational costs. We validate our methods on synthetic data and real-world datasets, including email communications within European academic communities. Our approach effectively reconstructs underlying networks, accounting for both excitation and diffusion influences. This work advances network reconstruction from count data in real-world scenarios.</div></div>","PeriodicalId":20050,"journal":{"name":"Physica D: Nonlinear Phenomena","volume":"477 ","pages":"Article 134705"},"PeriodicalIF":2.7,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

On the multi-component Fokas–Lenells system: KP reductions and various soliton solutions 关于多组分Fokas-Lenells系统：KP降低和各种孤子解决方案

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-05 DOI: 10.1016/j.physd.2025.134706

Han-Han Sheng , Bao-Feng Feng , Guo-Fu Yu

引用次数: 0

On the dynamics of the Craik–Okamoto and the Euler top 关于Craik-Okamoto和欧拉顶的动力学

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-03 DOI: 10.1016/j.physd.2025.134684

Jaume Llibre , Claudia Valls

引用次数: 0

A front-tracking/ghost-fluid method for the numerical simulations of Richtmyer–Meshkov Instability richmyer - meshkov不稳定性数值模拟的前跟踪/鬼流体方法

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-02 DOI: 10.1016/j.physd.2025.134696

Ryan Holley , Tulin Kaman

引用次数: 0

Quasi-fluxon bubble dynamics in a rapid oscillatory microwave field 快速振荡微波场中的准通量泡动力学

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-02 DOI: 10.1016/j.physd.2025.134704

Alicia G. Castro-Montes , Elram S. Figueroa , Juan F. Marín , Mónica A. García-Ñustes

引用次数: 0

A classification of solitons for the surface diffusion flow of entire graphs 整个图的表面扩散流的孤子分类

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-05-01 DOI: 10.1016/j.physd.2025.134702

Piotr Rybka , Glen Wheeler

引用次数: 0

Biological aggregations from spatial memory and nonlocal advection 来自空间记忆和非局部平流的生物聚集

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-04-29 DOI: 10.1016/j.physd.2025.134682

Di Liu , Jonathan R. Potts , Yurij Salmaniw , Junping Shi , Hao Wang

{"title":"Biological aggregations from spatial memory and nonlocal advection","authors":"Di Liu , Jonathan R. Potts , Yurij Salmaniw , Junping Shi , Hao Wang","doi":"10.1016/j.physd.2025.134682","DOIUrl":"10.1016/j.physd.2025.134682","url":null,"abstract":"<div><div>We investigate a nonlocal reaction–diffusion–advection model of a population of organisms that integrates spatial memory of previously visited locations and nonlocal detection in space, resulting in a coupled PDE–ODE system reflective of several models found in spatial ecology. Our study advances the mathematical understanding of such models by proving the existence and uniqueness of a global weak solution in one spatial dimension using an iterative approach. This result includes potentially discontinuous detection kernels, explicitly emphasizing the so-called ‘top-hat’ detection function, and does not place any restriction on the rate of advection, thereby addressing some analytical voids in the mathematical discourse on such models. A comprehensive spectral and stability analysis is also performed, providing analytical expressions for bifurcation values contingent on various model parameters, such as species advection rate, diffusion rate, memory uptake and decay rates. Unlike classical reaction–diffusion systems, the point spectrum may now include elements that have an infinite-dimensional kernel. We show the existence of such a point and that it remains negative, ensuring that it does not influence the stability of the constant steady state. Linear stability analysis then provides critical values for destabilizing the constant steady state. We explicitly describe the form of the non-constant steady state near these critical values and classify the nature of the pitchfork bifurcation as forward/backward and stable/unstable. To complement our analytical insights, we explore a targeted case study of three particular instances with the top-hat detection function. Using a pseudo-spectral method, we depict a numerical bifurcation diagram showing cases with sub or supercritical behaviour.</div></div>","PeriodicalId":20050,"journal":{"name":"Physica D: Nonlinear Phenomena","volume":"476 ","pages":"Article 134682"},"PeriodicalIF":2.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Lagrangian uncertainty quantification approach to validate ocean model datasets 验证海洋模式数据集的拉格朗日不确定性量化方法

IF 2.7 3区数学

Physica D: Nonlinear Phenomena Pub Date : 2025-04-28 DOI: 10.1016/j.physd.2025.134690

Guillermo García-Sánchez , Makrina Agaoglou , Evanne Marie Claire Smith , Ana Maria Mancho

引用次数: 0