# gst: curiosity-driven search for novel behaviors.

<< One of the first exciting things to do with a new physical system is to go exploring—to tune parameters and see what unexpected behaviors the system is capable of. >>️

AA << combine active and unsupervised learning for automated exploration of nonequilibrium systems with unknown order parameters. (They) iteratively use active learning based on current order parameters to expand the library of behaviors and relearn order parameters based on this expanded library. (They) demonstrate the utility of this approach in Kuramoto models of increasing complexity. In addition to reproducing known phases, (AA) reveal previously unknown behavior and related order parameters, and demonstrate how to align search with human intuition. >>️

Martin J. Falk, Finnegan D. Roach, et al. Curiosity-driven search for novel nonequilibrium behaviors. Phys. Rev. Research 6, 033052. Jul 11, 2024. 

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033052  

Also: curiosity, behav, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, curiosity, behav, transition

# game: flocking by turning away.

<< Flocking, as paradigmatically exemplified by birds, is the coherent collective motion of active agents. As originally conceived, flocking emerges through alignment interactions between the agents. >>️

AA << report that flocking can also emerge through interactions that turn agents away from each other. >>

<< Whereas repulsion often leads to motility-induced phase separation of active particles, here it combines with turn-away torques to produce flocking. Therefore, (AA) findings bridge the classes of aligning and nonaligning active matter. (Their) results could help to reconcile the observations that cells can flock despite turning away from each other via contact inhibition of locomotion.  >>️

AA << work shows that flocking is a very robust phenomenon that arises even when the orientational interactions would seem to prevent it. >>️

️

Suchismita Das, Matteo Ciarchi, Ziqi Zhou, Jing Yan, Jie Zhang, Ricard Alert. Flocking by Turning Away. Phys. Rev. X 14, 031008. Jul 12, 2024.

https://journals.aps.org/prx/abstract/10.1103/PhysRevX.14.031008

Also: Janus, in FonT 

https://flashontrack.blogspot.com/search?q=janus 

Also: game, flock, in https://www.inkgmr.net/kwrds.html 

Keywords: game, flock, Janus

FonT: an approach of this type could hypothetically generate intriguing, bizarre, unexpected game scenarios in various other contexts ...

# gst: apropos of the transition of order from chaos, a universal behavior near a critical point.

<< As the Reynolds number is increased, a laminar fluid flow becomes turbulent, and the range of time and length scales associated with the flow increases. Yet, in a turbulent reactive flow system, as we increase the Reynolds number, (AA) observe the emergence of a single dominant timescale in the acoustic pressure fluctuations, as indicated by its loss of multifractality. >>️

AA << study the evolution of short-time correlated dynamics between the acoustic field and the flame in the spatiotemporal domain of the system.   >>️

<< the susceptibility of the order parameter, correlation length, and correlation time diverge at a critical point between chaos and order. (AA) results show that the observed emergence of order from chaos is a continuous phase transition (..) the critical exponents characterizing this transition fall in the universality class of directed percolation. >>️

The << paper demonstrates how a real-world complex, nonequilibrium turbulent reactive flow system exhibits universal behavior near a critical point. >>️

Sivakumar Sudarsanan, Amitesh Roy, et al. Emergence of order from chaos through a continuous phase transition in a turbulent reactive flow system. Phys. Rev. E 109, 064214. Jun 20, 2024. 

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.109.064214

Also: order, chaos, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, order, chaos, transition 

# gst: discontinuous transition to chaos in a canonical random neural network

AA << study a paradigmatic random recurrent neural network introduced by Sompolinsky, Crisanti, and Sommers (SCS). In the infinite size limit, this system exhibits a direct transition from a homogeneous rest state to chaotic behavior, with the Lyapunov exponent gradually increasing from zero. (AA)  generalize the SCS model considering odd saturating nonlinear transfer functions, beyond the usual choice 𝜙⁡(𝑥)=tanh⁡𝑥. A discontinuous transition to chaos occurs whenever the slope of 𝜙 at 0 is a local minimum [i.e., for 𝜙′′′⁢(0)>0]. Chaos appears out of the blue, by an attractor-repeller fold. Accordingly, the Lyapunov exponent stays away from zero at the birth of chaos. >>

In the figure 7 << the pink square is located at the doubly degenerate point (𝑔,𝜀)=(1,1/3). >>️️

Diego Pazó. Discontinuous transition to chaos in a canonical random neural network. Phys. Rev. E 110, 014201. July 1, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.014201

Also: chaos, random, network, transition, neuro, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chaos, random, network, transition, neuro

# gst: the hypothesis of the onset of extreme events via an attractor merging crisis.

AA << investigate the temporal dynamics of the Ikeda Map with Balanced Gain and Loss and in the presence of feedback loops with saturation nonlinearity. From the bifurcation analysis, (They) find that the temporal evolution of optical power undergoes period quadrupling at the exceptional point (EP) of the system and beyond that, chaotic dynamics emerge in the system and this has been further corroborated from the Largest Lyapunov Exponent (LLE) of the model. >>

<< For a closer inspection, (AA) analyzed the parameter basin of the system, which further leads to (their) inference that the Ikeda Map with Balanced Gain and Loss exhibits the emergence of chaotic dynamics beyond the exceptional point (EP). >>

<< Furthermore, (AA) find that the temporal dynamics beyond the EP regime leads to the onset of Extreme Events (EE) in this system via attractor merging crisis. >>️

Jyoti Prasad Deka, Amarendra K. Sarma. Temporal Dynamics beyond the Exceptional Point in the Ikeda Map with Balanced Gain and Loss. arXiv: 2406.17783 [eess.SP]. May 13, 2024. 

https://arxiv.org/abs/2406.17783 

Also: chaos, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chaos, chaotic dynamics, attractor merging crisis 

# gst: when generalized diffusion could result from stochastic processes.

<< Despite the success of fractional Brownian motion (fBm) in modeling systems that exhibit anomalous diffusion due to temporal correlations, recent experimental and theoretical studies highlight the necessity for a more comprehensive approach of a generalization that incorporates heterogeneities in either the tracers or the environment. >>

AA present << a modification of Lévy's representation of fBm for the case in which the generalized diffusion coefficient is a stochastic process. (They) derive analytical expressions for the autocovariance function and both ensemble- and time-averaged mean squared displacements. Further, (AA)  validate the efficacy of the developed framework in two-state systems, comparing analytical asymptotic expressions with numerical simulations. >>️

Adrian Pacheco-Pozo, Diego Krapf. Fractional Brownian motion with fluctuating diffusivities. Phys. Rev. E 110, 014105. Jul 1, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.014105

Also: disorder, fluctuations, network, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, fractional Brownian motion, fBm, Lévy, disorder, fluctuations, anomalous, network, transition

# gst: the strangeness of networks, the hypothesis of a connection between the kinetics of networks and anomalous transport theory.

<< Many real-world networks change over time. Think, for example, of social interactions, gene activation in a cell, or strategy making in financial markets, where connections and disconnections occur all the time. >>

AA team << has gained groundbreaking insights into this problem by recasting the discrete dynamics of a network as a continuous time series (..). In doing so, the researchers have discovered that if the breaking and forming of links are represented as a particle moving in a suitable geometric space, then its motion is subdiffusive—that is, slower than it would be if it diffused normally. What’s more, the particles’ motions are well described by fractional Brownian motion, a generalization of Einstein’s classic model. This feat establishes a profound connection between the kinetics of time-varying or “temporal” networks and anomalous transport theory, opening fresh prospects for developing predictive equations of motion for networks. >>️

Ivan Bonamassa. Strange Kinetics Shape Network Growth. Physics 17, 96. Jun 17, 2024.

https://physics.aps.org/articles/v17/96

Evangelos S. Papaefthymiou, Costas Iordanou, Fragkiskos Papadopoulos. Fundamental Dynamics of Popularity-Similarity Trajectories in Real Networks. Phys. Rev. Lett. 132, 257401. Jun 17, 2024. 

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.257401

Also: network, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, network, transition