Pratt Hatch (powderbelief42)

We investigated the spectra of resonances of four-vertex microwave networks simulating both quantum graphs with preserved and with partially violated time-reversal invariance before and after an edge switch operation. We show experimentally that under the edge switch operation, the spectra of the microwave networks with preserved time-reversal symmetry are level-1 interlaced, i.e., ν_n-r≤ν[over ̃]_n≤ν_n+r, where r=1, in agreement with the recent theoretical predictions of Aizenman et al. [M. Aizenman, H. Schanz, U. Smilansky, and S. Warzel, Acta Phys. Pol. A 132, 1699 (2017)ATPLB60587-424610.12693/APhysPolA.132.1699]. Here, we denote by ν_n_n=1^∞ and ν[over ̃]_n_n=1^∞ the spectra of microwave networks before and after the edge switch transformation. We demonstrate that the experimental distribution P(ΔN) of the spectral shift ΔN is close to the theoretical one. Furthermore, we show experimentally that in the case of the four-vertex networks with partially violated time-reversal symmetry, the spectra are level-1 interlaced. Our experimental results are supplemented by the numerical calculations performed for quantum graphs with violated time-reversal symmetry. In this case, the edge switch transformation also leads to the spectra which are level-1 interlaced. Moreover, we demonstrate that for microwave networks simulating graphs with violated time-reversal symmetry, the experimental distribution P(ΔN) of the spectral shift ΔN agrees, within the experimental uncertainty, with the numerical one.We discuss the derivation and the solutions of integrodifferential equations (variable-order time-fractional diffusion equations) following as continuous limits for lattice continuous time random walk schemes with power-law waiting-time probability density functions whose parameters are position-dependent. We concentrate on subdiffusive cases and discuss two situations as examples A system consisting of two parts with different exponents of subdiffusion, and a system in which the subdiffusion exponent changes linearly from one end of the interval to another one. In both cases we compare the numerical solutions of generalized master equations describing the process on the lattice to the corresponding solutions of the continuous equations, which follow by exact solution of the corresponding equations in the Laplace domain with subsequent numerical inversion using the Gaver-Stehfest algorithm.Percolation and fracture propagation in disordered solids represent two important problems in science and engineering that are characterized by phase transitions loss of macroscopic connectivity at the percolation threshold p_c and formation of a macroscopic fracture network at the incipient fracture point (IFP). Percolation also represents the fracture problem in the limit of very strong disorder. An important unsolved problem is accurate prediction of physical properties of systems undergoing such transitions, given limited data far from the transition point. There is currently no theoretical method that can use limited data for a region far from a transition point p_c or the IFP and predict the physical properties all the way to that point, including their location. We present a deep neural network (DNN) for predicting such properties of two- and three-dimensional systems and in particular their percolation probability, the threshold p_c, the elastic moduli, and the universal Poisson ratio at p_c. All the predictions are in excellent agreement with the data. In particular, the DNN predicts correctly p_c, even though the training data were for the state of the systems far from p_c. This opens up the possibility of using the DNN for predicting physical properties of many types of disordered materials that undergo phase transformation, for which limited data are available for only far from the transition point.I study the statistical description of a small quantum system, which is coupled to a large quantum environment in a generic form and with a gene