George Haller,Roshan S Kaundinya
George Haller
We extend the theory of spectral submanifolds (SSMs) to general non-autonomous dynamical systems that are either weakly forced or slowly varying. Examples of such systems arise in structural dynamics, fluid-structure interactions, and contr...
F Ronetti,B Bertin-Johannet,A Popoff et al.
F Ronetti et al.
In this short review (written to celebrate David Campbell's 80th birthday), we provide a theoretical description of quantum transport in nanoscale systems in the presence of single-electron excitations generated by Lorentzian voltage drives...
Distinguishing between fractional Brownian motion with random and constant Hurst exponent using sample autocovariance-based statistics [0.03%]
基于样本自协方差统计量区分Hurst指数随机和恒定的分数布朗运动
Aleksandra Grzesiek,Janusz Gajda,Samudrajit Thapa et al.
Aleksandra Grzesiek et al.
Fractional Brownian motion (FBM) is a canonical model for describing dynamics in various complex systems. It is characterized by the Hurst exponent, which is responsible for the correlation between FBM increments, its self-similarity proper...
Adaptive sampling physics-informed neural network method for high-order rogue waves and parameters discovery of the (2 + 1)-dimensional CHKP equation [0.03%]
自适应抽样物理启发式神经网络法求解（2+1）维CHKP方程的高阶罗斯波波及参数确定问题
Hongli An,Kaijie Xing,Yao Chen
Hongli An
Rogue waves are important physical phenomena, which have wide applications in nonlinear optics, hydrodynamics, Bose-Einstein condensates, and oceanic and atmospheric dynamics. We find that when using the original PINNs to study rogue waves ...
Joshin John Bejoy,G Ambika
Joshin John Bejoy
We present a study on the spatiotemporal pattern underlying the climate dynamics in various locations spread over India, including the Himalayan region, coastal region, and central and northeastern parts of India. We try to capture the vari...
Machine learning approach to detect dynamical states from recurrence measures [0.03%]
基于复发性测度检测动力学状态的机器学习方法
Dheeraja Thakur,Athul Mohan,G Ambika et al.
Dheeraja Thakur et al.
We integrate machine learning approaches with nonlinear time series analysis, specifically utilizing recurrence measures to classify various dynamical states emerging from time series. We implement three machine learning algorithms: Logisti...
Collective behaviors of animal groups may stem from visual lateralization-Tending to obtain information through one eye [0.03%]
动物群体的行为可能源自视觉偏侧化现象——倾向于用一眼获取信息
Jian Gao,Changgui Gu,Yongshang Long et al.
Jian Gao et al.
Animal groups exhibit various captivating movement patterns, which manifest as intricate interactions among group members. Several models have been proposed to elucidate collective behaviors in animal groups. These models achieve a certain ...
E Rybalova,N Nikishina,G Strelkova
E Rybalova
We explore numerically how additive Lévy noise influences the spatiotemporal dynamics of a neural network of nonlocally coupled FitzHugh-Nagumo oscillators. Without noise, the network can exhibit various partial or cluster synchronization ...
Lei Chen,Yanpeng Zhu,Fanyuan Meng et al.
Lei Chen et al.
The failures of individual agents can significantly impact the functionality of associated groups in interconnected systems. To reveal these impacts, we develop a threshold model to investigate cascading failures in double-layer hypergraphs...
Nonlinear spreading behavior across multi-platform social media universe [0.03%]
跨越多平台社交媒介宇宙的非线性传播现象
Chenkai Xia,Neil F Johnson
Chenkai Xia
Understanding how harmful content (mis/disinformation, hate, etc.) manages to spread among online communities within and across social media platforms represents an urgent societal challenge. We develop a non-linear dynamical model for such...