An unusual phase transition in a non-Hermitian Su-Schrieffer-Heeger model.

This article studies a non-Hermitian Su-Schrieffer-Heeger model which has periodically staggered Hermitian and non-Hermitian dimers. The changes in topological phases of the considered chiral symmetric model with respect to the introduced non-Hermiticity are studied where we find that the system supports only complex eigenspectra for all values of ≠ 0 and it stabilizes only non-trivial insulating phase for higher loss-gain strength. Even if the system acts as a trivial insulator in the Hermitian limit, the increase in loss-gain strength induces phase transition to non-trivial insulating phase through a (gapless) semi-metallic phase.

Stimulus-induced dynamical states in an adaptive network with symmetric adaptation.

We consider an adaptive network of identical phase oscillators with the symmetric adaptation rule for the evolution of the connection weights under the influence of an external force. We show that the adaptive network exhibits a plethora of self-organizing dynamical states such as the two-cluster state, multiantipodal clusters, splay cluster, splay chimera, forced entrained state, chimera state, bump state, coherent, and incoherent states in the two-parameter phase diagrams. The intriguing structures of the frequency clusters and instantaneous phases of the oscillators characterize the distinct self-organized synchronized and partial synchronized states.

Hebbian and anti-Hebbian adaptation-induced dynamical states in adaptive networks.

We investigate the interplay of an external forcing and an adaptive network, whose connection weights coevolve with the dynamical states of the phase oscillators. In particular, we consider the Hebbian and anti-Hebbian adaptation mechanisms for the evolution of the connection weights. The Hebbian adaptation manifests several interesting partially synchronized states, such as phase and frequency clusters, bump state, bump frequency phase clusters, and forced entrained clusters, in addition to the completely synchronized and forced entrained states.

Data driven soliton solution of the nonlinear Schrödinger equation with certain P T-symmetric potentials via deep learning.

We investigate the physics informed neural network method, a deep learning approach, to approximate soliton solution of the nonlinear Schrödinger equation with parity time symmetric potentials. We consider three different parity time symmetric potentials, namely, Gaussian, periodic, and Rosen-Morse potentials. We use the physics informed neural network to solve the considered nonlinear partial differential equation with the above three potentials.

Exotic states induced by coevolving connection weights and phases in complex networks.

We consider an adaptive network, whose connection weights coevolve in congruence with the dynamical states of the local nodes that are under the influence of an external stimulus. The adaptive dynamical system mimics the adaptive synaptic connections common in neuronal networks. The adaptive network under external forcing displays exotic dynamical states such as itinerant chimeras whose population density of coherent and incoherent domains coevolves with the synaptic connection, bump states, and bump frequency cluster states, which do not exist in adaptive networks without forcing.