Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numeri...
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Many systems in physics, chemistry and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example linear dynamics of a stable focus w...
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We demonstrate,both analytically and experimentally,free-space pin-like optical vortex beams (POVBs). Such angular-momentum-carrying beams feature tunable peak intensity and undergo robust antidiffracting propagation,...
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We demonstrate,both analytically and experimentally,free-space pin-like optical vortex beams (POVBs). Such angular-momentum-carrying beams feature tunable peak intensity and undergo robust antidiffracting propagation,realized by judiciously modulating both the amplitude and the phase profile of a standard laser ***,they are generated by superimposing a radially symmetric power-law phase on a helical phase structure,which allows the inclusion of an orbital angular momentum term to the POVBs. During propagation in free space,these POVBs initially exhibit autofocusing dynamics,and subsequently their amplitude patterns morph into a high-order Bessel-like profile characterized by a hollow core and an annular main lobe with a constant or tunable width during propagation. In contrast with numerous previous endeavors on Bessel beams,our work represents the first demonstration of long-distance free-space generation of optical vortex "pins" with their peak intensity evolution controlled by the impressed amplitude structure. Both the Poynting vectors and the optical radiation forces associated with these beams are also numerically analyzed,revealing novel properties that may be useful for a wide range of applications.
Risk-driven behavior provides a feedback mechanism through which individuals both shape and are collectively affected by an epidemic. We introduce a general and flexible compartmental model to study the effect of hete...
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We discuss the challenges of motivating, constructing, and quantizing a canonically normalized inflationary perturbation in spatially curved universes. We show that this has historically proved challenging due to the ...
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We discuss the challenges of motivating, constructing, and quantizing a canonically normalized inflationary perturbation in spatially curved universes. We show that this has historically proved challenging due to the interaction of nonadiabaticity with spatial curvature. We construct a novel curvature perturbation that is canonically normalized in the sense of its equation of motion and is unique up to a single scalar parameter. With this construction it becomes possible to set initial conditions invariant under canonical transformations, overcoming known ambiguities in the literature. This corrected quantization has potentially observational consequences via modifications to the primordial power spectrum at large angular scales, as well as theoretical implications for quantization procedures in curved cosmologies filled with a scalar field.
We demonstrate a relation between Nielsen’s approach toward circuit complexity and Krylov complexity through a particular construction of quantum state space geometry. We start by associating Kähler structures o...
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We demonstrate a relation between Nielsen’s approach toward circuit complexity and Krylov complexity through a particular construction of quantum state space geometry. We start by associating Kähler structures on the full projective Hilbert space of low rank algebras. This geometric structure of the states in the Hilbert space ensures that every unitary transformation of the associated algebras leave the metric and the symplectic forms invariant. We further associate a classical matter free Jackiw-Teitelboim gravity model with these state manifolds and show that the dilaton can be interpreted as the quantum mechanical expectation values of the symmetry generators. On the other hand, we identify the dilaton with the spread complexity over a Krylov basis thereby proposing a geometric perspective connecting two different notions of complexity.
Deep learning has shown successful application in visual recognition and certain artificial intelligence tasks. It is mainly considered as a powerful tool with high flexibility to approximate functions. This paper pro...
Deep learning has shown successful application in visual recognition and certain artificial intelligence tasks. It is mainly considered as a powerful tool with high flexibility to approximate functions. This paper proposes a generalized NURBS based approach to solve nonlinear partial differential equations (PDEs) on arbitrary complex-geometry domains by using physics-informed neural networks (PINNs). Our approach is based on a posteriori error estimation in which the adjoint problem is solved for the error localization to formulate an error estimator within the framework of neural network. An efficient and easy to implement algorithm is developed to obtain a posteriori error estimate for multiple goal functionals by employing the dual-weighted residual approach, which is followed by the computation of both primal and adjoint solutions using the neural network. The present study shows that such a data-driven model based learning has superior approximation of quantities of interest even with relatively less training data. Moreover, we illustrate the versatility of activation functions in achieving better learning capabilities and improving convergence rates, especially at the early training stage, and also in increasing solutions accuracies. The novel algorithmic developments are substantiated with several numerical test examples. It has been demonstrated that deep neural networks have distinct advantages over shallow neural networks, and the techniques for enhancing convergence have also been reviewed.
In this study, N-Structured based InAs/AlSb/GaSb Type-II Superlattice pbin type detector structures are investigated. These systems make absorption in infrared region in electromagnetic spectrum as detectors. Structur...
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Many of our previous studies have discussed the shock response of symmetrical grain boundaries in iron *** this paper, the molecular dynamics simulation of an iron bicrystal containing Σ3 [110] asymmetry tilt grain b...
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Many of our previous studies have discussed the shock response of symmetrical grain boundaries in iron *** this paper, the molecular dynamics simulation of an iron bicrystal containing Σ3 [110] asymmetry tilt grain boundary(ATGB) under shock-loading is performed. We find that the shock response of asymmetric grain boundaries is quite different from that of symmetric grain boundaries. Especially, our simulation proves that shock can induce migration of asymmetric grain boundary in iron. We also find that the shape and local structure of grain boundary(GB) would not be changed during shock-induced migration of Σ3 [110] ATGB, while the phase transformation near the GB could affect migration of GB. The most important discovery is that the shock-induced shear stress difference between two sides of GB is the key factor leading to GB migration. Our simulation involves a variety of piston velocities, and the migration of GB seems to be less sensitive to the piston velocity. Finally, the kinetics of GB migration at lattice level is discussed. Our work firstly reports the simulation of shock-induced grain boundary migration in iron. It is of great significance to the theory of GB migration and material engineering.
Widely recognized as a thermally activated process, atomic stick-slip friction has been typically explained by Prandtl-Tomlinson model with thermal activation. Despite the limited success, theoretical predictions from...
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Widely recognized as a thermally activated process, atomic stick-slip friction has been typically explained by Prandtl-Tomlinson model with thermal activation. Despite the limited success, theoretical predictions from the classic model are primarily based on a one-dimensional (1D) assumption, which is generally not compatible with real experiments that are two-dimensional (2D) in nature. In this letter, a theoretical model based on 2D transition state theory has been derived and confirmed to be able to capture the 2D slip kinetics in atomic-scale friction experiments on crystalline surface with a hexagonal energy landscape. Moreover, we propose a reduced scheme that enables extraction of intrinsic interfacial parameters from 2D experiments approximately using the traditional 1D model. The 2D model provides a theoretical tool for understanding the rich kinetics of atomic-scale friction or other phenomena involving higher dimensional transitions.
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