Chaos最新文献_第6页

Brownian motion with stochastic energy renewals. 具有随机能量更新的布朗运动。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0285588

Ion Santra, Kristian Stølevik Olsen

引用次数: 0

Analysis of influencing factors and the most probable transition pathway in the narrow escape problem for molecular systems based on deep learning method. 基于深度学习方法的分子系统窄逃逸问题影响因素及最可能过渡途径分析

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0288558

Jiangyan Liu, Ming Yi, Ting Gao, Xiaoli Chen

{"title":"Analysis of influencing factors and the most probable transition pathway in the narrow escape problem for molecular systems based on deep learning method.","authors":"Jiangyan Liu, Ming Yi, Ting Gao, Xiaoli Chen","doi":"10.1063/5.0288558","DOIUrl":"https://doi.org/10.1063/5.0288558","url":null,"abstract":"This study employs physics-informed neural networks (PINNs) to investigate the narrow escape problem in irregular domains, aiming to understand how key parameters influence molecular escape behavior and to analyze the most probable transition pathway of molecules. We focus on two critical metrics: mean exit time and escape probability, characterizing escape behavior in stochastic systems. Using PINNs, we effectively address the domain's complexities and examine the effects of parameters such as diffusion coefficient, angular velocity, annular area, and absorption domain size on mean exit time and escape probability. Moreover, by computing the most probable transition pathway, we further uncovered the underlying mechanisms that govern molecular motion in complex environments. An interesting observation was that increasing the diffusion coefficient expanded the high-probability escape region but decreased the overall escape probability. The results provide valuable insights for optimizing escape efficiency in practical applications and highlight the potential of PINNs for studying complex diffusion problems.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999729","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

Incorporating coupling knowledge into echo state networks for learning spatiotemporally chaotic dynamics. 将耦合知识引入回声状态网络学习时空混沌动力学。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0273343

Kuei-Jan Chu, Nozomi Akashi, Akihiro Yamamoto

{"title":"Incorporating coupling knowledge into echo state networks for learning spatiotemporally chaotic dynamics.","authors":"Kuei-Jan Chu, Nozomi Akashi, Akihiro Yamamoto","doi":"10.1063/5.0273343","DOIUrl":"https://doi.org/10.1063/5.0273343","url":null,"abstract":"Machine learning methods have shown promise in learning chaotic dynamical systems, enabling model-free short-term prediction and attractor reconstruction. However, when applied to large-scale, spatiotemporally chaotic systems, purely data-driven machine learning methods often suffer from inefficiencies, as they require a large learning model size and a massive amount of training data to achieve acceptable performance. To address this challenge, we incorporate the spatial coupling structure of the target system as an inductive bias in the network design. Specifically, we introduce physics-guided clustered echo state networks, leveraging the efficiency of the echo state networks (ESNs) as a base model. Experimental results on benchmark chaotic systems demonstrate that our physics-informed method outperforms existing echo state network models in learning the target chaotic systems. Additionally, we numerically demonstrate that leveraging coupling knowledge into ESN models can enhance their robustness to variations of training and target system conditions. We further show that our proposed model remains effective even when the coupling knowledge is imperfect or extracted directly from time series data. We believe that this approach has the potential to enhance other machine learning methods.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145074291","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

Explosive adoption of corrupt behaviors in social systems with higher-order interactions. 在具有高阶互动的社会系统中，腐败行为的爆炸性采用。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0292972

Elisa Bretón-Fuertes, Clara Clemente-Marcuello, Verónica Sanz-Arqué, Gabriela Tomás-Delgado, Santiago Lamata-Otín, Hugo Pérez-Martínez, Jesús Gómez-Gardeñes

{"title":"Explosive adoption of corrupt behaviors in social systems with higher-order interactions.","authors":"Elisa Bretón-Fuertes, Clara Clemente-Marcuello, Verónica Sanz-Arqué, Gabriela Tomás-Delgado, Santiago Lamata-Otín, Hugo Pérez-Martínez, Jesús Gómez-Gardeñes","doi":"10.1063/5.0292972","DOIUrl":"https://doi.org/10.1063/5.0292972","url":null,"abstract":"Human behaviors in social systems are often shaped by group pressure and collective norms, especially since the rise of social media platforms. However, in the context of adopting misbehaviors, most existing contagion models rely on pairwise interactions and thus fail to capture group-level dynamics. To fill this gap, we introduce a higher-order extension of the honesty-corruption-ostracism model to study the emergence of systemic corruption in populations where individuals interact through group structures. The model incorporates contagion-like transitions mediated by hyperedges of arbitrary order, capturing the influence of peer pressure in group settings. Analytical and numerical results show that higher-order interactions induce discontinuous (explosive) transitions between fully honest and fully corrupt regimes, separated by a bistable phase. This abrupt behavior disappears in the pairwise limit, highlighting the destabilizing effect of group interactions. Furthermore, we establish a general correspondence between our model and broader classes of social contagion dynamics with symmetry breaking, recovering previous results as limiting cases. These findings underscore the critical role of a higher-order structure in shaping behavioral adoption processes and the stability of social systems.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144991640","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

Effects of symmetric and asymmetric habitat loss on species coexistence in cyclic competition. 对称和非对称生境丧失对循环竞争中物种共存的影响。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0295840

Hui Dai, Xiaoyue Wang, Xiaowen Dai, Lei Shi

引用次数: 0

Cluster synchronization via graph Laplacian eigenvectors. 基于图拉普拉斯特征向量的聚类同步。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0280142

Tobias Timofeyev, Alice Patania

{"title":"Cluster synchronization via graph Laplacian eigenvectors.","authors":"Tobias Timofeyev, Alice Patania","doi":"10.1063/5.0280142","DOIUrl":"https://doi.org/10.1063/5.0280142","url":null,"abstract":"Almost equitable partitions (AEPs) have been linked to cluster synchronization in oscillatory systems, highlighting the importance of structure in collective network dynamics. We provide a general spectral framework that formalizes this connection, showing how eigenvectors associated with AEPs span a subspace of the Laplacian spectrum that governs partition-induced synchronization behavior. This offers a principled reduction of network dynamics, allowing clustered states to be understood in terms of quotient graph projections. Our approach clarifies the conditions under which transient hierarchical clustering and multi-frequency synchronization emerge and connects these dynamical phenomena directly to network symmetry and community structure. In doing so, we bridge a critical gap between static topology and dynamic behavior, namely, the lack of a spectral method for analyzing synchronization in networks that exhibit exact or approximate structural regularity. Perfect AEPs are rare in real-world networks since most have some degree of irregularity or noise. We define relaxation of an AEP we call a quasi-equitable partition at level δ (δ-QEP). δ-QEPs can preserve many of the clustering-relevant properties of AEPs while tolerating structural imperfections and noise. This extension enables us to describe synchronization behavior in more realistic scenarios, where ideal symmetries are rarely present. Our findings have important implications for understanding synchronization patterns in real-world networks, from neural circuits to power grids.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999721","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

Particle model of creation and annihilation captures termination dynamics of spiral defect chaos. 产生和湮灭的粒子模型捕捉了螺旋缺陷混沌的终止动力学。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0277113

Timothy J Tyree, Michael Reiss, Wouter-Jan Rappel

引用次数: 0

Forced symmetry-breaking in networks with dihedral symmetry. 二面体对称网络中的强迫对称性破缺。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0288584

Antonio Palacios, Samir Sahoo, Madeline Parker, Aradhana Singh, Sheksha Dudekula

引用次数: 0

Nonlocal-coupling-based control of coherence resonance. 基于非局部耦合的相干共振控制。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0288025

Aleksey Ryabov, Elena Rybalova, Andrei Bukh, Tatiana E Vadivasova, Vladimir V Semenov

引用次数: 0

Energy of quasi-zero stiffness energy harvester under strange non-chaotic attractor. 奇异非混沌吸引子作用下准零刚度能量收集器的能量。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0281427

Prakash Duraisamy, Dianavinnarasi Joseph, Suresh Kumarasamy, Balamurali Ramakrishnan

{"title":"Energy of quasi-zero stiffness energy harvester under strange non-chaotic attractor.","authors":"Prakash Duraisamy, Dianavinnarasi Joseph, Suresh Kumarasamy, Balamurali Ramakrishnan","doi":"10.1063/5.0281427","DOIUrl":"https://doi.org/10.1063/5.0281427","url":null,"abstract":"This study investigates the Quasi-Zero Stiffness Energy Harvester (QZEH), a nonlinear, multi-stable system designed for enhanced energy extraction from vibrating mechanical devices. Nonlinear harvesters, like QZEH and bistable systems, operate over a wider frequency range, effectively capturing energy from broadband or irregular inputs, but can lead to complex behaviors. Maintaining a quasi-periodically forced QZEH in a periodic state is challenging due to the intricate interaction between the system's nonlinear dynamics and the input's incommensurate frequencies. While periodic solutions are typically associated with higher energy yields than chaotic ones, we report a novel dynamical domain under quasi-periodic excitation. Surprisingly, this regime, characterized by a strange non-chaotic attractor, demonstrates a significantly higher energy harvesting efficiency than chaotic motion. This finding challenges conventional expectations and opens new avenues for optimizing energy harvesters. We examine robustness under practical conditions by analyzing the effects of additive white noise on the QZEH system. The results show that increasing noise intensity progressively erodes the basin of strange nonchaotic attractors, while energy harvesting performance remains stable in the single-attractor regime. This discovery represents a significant advancement in energy harvesting technologies, offering a pathway to achieve higher energy extraction by utilizing nontraditional dynamical behaviors.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145112231","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