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1. Parametric Dependence of Large Disturbance Response for Vector Fields with Event-Selected Discontinuities

   https://doi.org/10.23919/ECC.2019.8796029  

   Fisher, Michael W.; Hiskens, Ian A. (June 2019, 2019 18th European Control Conference)

   The ability of a nonlinear system to recover from a large disturbance to a desired stable equilibrium point depends on system parameter values, which are often uncertain and time varying. A particular disturbance acting for a finite time can be modeled as an implicit map that takes a parameter value to its corresponding post disturbance initial condition in state space. The system recovers when the post-disturbance initial condition lies inside the region of attraction of the stable equilibrium point. Critical parameter values are defined to be parameter values whose corresponding post-disturbance initial condition lies on the boundary of the region of attraction. Computing such values is important in numerous applications because they represent the boundary between desirable and undesirable system behavior. Many realistic system models involve controller clipping limits and other forms of switching. Furthermore, these hybrid dynamics are closely linked to the ability of a system to recover from disturbances. The paper develops theory which underpins a novel algorithm for numerically computing critical parameter values for nonlinear systems with clipping limits and switching. For an almost generic class of vector fields with event-selected discontinuities, it is shown that the boundary of the region of attraction is equal to a union of the stable manifolds of the equilibria and periodic orbits it contains, and that this decomposition persists and the boundary varies continuously under small changes in parameter. 

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2. Global forecasts in reservoir computers

   https://doi.org/10.1063/5.0181694  

   Harding, S; Leishman, Q; Lunceford, W; Passey, D J; Pool, T; Webb, B (February 2024, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   A reservoir computer is a machine learning model that can be used to predict the future state(s) of time-dependent processes, e.g., dynamical systems. In practice, data in the form of an input-signal are fed into the reservoir. The trained reservoir is then used to predict the future state of this signal. We develop a new method for not only predicting the future dynamics of the input-signal but also the future dynamics starting at an arbitrary initial condition of a system. The systems we consider are the Lorenz, Rossler, and Thomas systems restricted to their attractors. This method, which creates a global forecast, still uses only a single input-signal to train the reservoir but breaks the signal into many smaller windowed signals. We examine how well this windowed method is able to forecast the dynamics of a system starting at an arbitrary point on a system’s attractor and compare this to the standard method without windows. We find that the standard method has almost no ability to forecast anything but the original input-signal while the windowed method can capture the dynamics starting at most points on an attractor with significant accuracy. 

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3. Continuous N\'{e}el-VBS quantum phase transition in non-local one-dimensional systems with SO(3) symmetry

   https://doi.org/10.21468/SciPostPhys.10.2.033  

   Jian, Chao-Ming; Xu, Yichen; Wu, Xiao-Chuan; Xu, Cenke (January 2021, SciPost Physics)

   null (Ed.)

   One dimensional (1d) interacting systems with local Hamiltonianscan be studied with various well-developed analytical methods.Recently novel 1d physics was found numerically in systems witheither spatially nonlocal interactions, or at the 1d boundary of2d quantum critical points, and the critical fluctuation in thebulk also yields effective nonlocal interactions at the boundary.This work studies the edge states at the 1d boundary of 2dstrongly interacting symmetry protected topological (SPT) states,when the bulk is driven to a disorder-order phase transition. Wewill take the 2d Affleck-Kennedy-Lieb-Tasaki (AKLT) state as anexample, which is a SPT state protected by the SO(3) spinsymmetry and spatial translation. We found that the original(1+1)d boundary conformal field theory of the AKLT state isunstable due to coupling to the boundary avatar of the bulkquantum critical fluctuations. When the bulk is fixed at thequantum critical point, within the accuracy of our expansionmethod, we find that by tuning one parameter at the boundary,there is a generic direct transition between the long rangeantiferromagnetic Néel order and the valence bond solid (VBS)order. This transition is very similar to the Néel-VBStransition recently found in numerical simulation of a spin-1/2chain with nonlocal spatial interactions. Connections between ouranalytical studies and recent numerical results concerning theedge states of the 2d AKLT-like state at a bulk quantum phasetransition will also be discussed. 

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4. Mean-field theory for double-well systems on degree-heterogeneous networks

   https://doi.org/10.1098/rspa.2022.0350  

   Kundu, Prosenjit; MacLaren, Neil G.; Kori, Hiroshi; Masuda, Naoki (August 2022, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   Many complex dynamical systems in the real world, including ecological, climate, financial and power-grid systems, often show critical transitions, or tipping points, in which the system’s dynamics suddenly transit into a qualitatively different state. In mathematical models, tipping points happen as a control parameter gradually changes and crosses a certain threshold. Tipping elements in such systems may interact with each other as a network, and understanding the behaviour of interacting tipping elements is a challenge because of the high dimensionality originating from the network. Here, we develop a degree-based mean-field theory for a prototypical double-well system coupled on a network with the aim of understanding coupled tipping dynamics with a low-dimensional description. The method approximates both the onset of the tipping point and the position of equilibria with a reasonable accuracy. Based on the developed theory and numerical simulations, we also provide evidence for multistage tipping point transitions in networks of double-well systems. 

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5. Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator

   https://doi.org/10.3390/math12060803  

   Liu, Shuo; Chen, Xu; Di, Xuan (March 2024, Mathematics)

   This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of population dynamics and a backward PDE for a representative agent’s optimal control. Existing work mainly focus on solving the mean field game equilibrium (MFE) of the MFG system when given fixed boundary conditions, including the initial population state and terminal cost. To obtain MFE efficiently, particularly when the initial population density and terminal cost vary, we utilize a physics-informed neural operator (PINO) to tackle the forward–backward PDEs. A learning algorithm is devised and its performance is evaluated on one application domain, which is the autonomous driving velocity control. Numerical experiments show that our method can obtain the MFE accurately when given different initial distributions of vehicles. The PINO exhibits both memory efficiency and generalization capabilities compared to physics-informed neural networks (PINNs). 

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