Light focusing by silicon nanosphere structures under conditions of magnetic dipole and quadrupole resonances

Metalens is a planar device for light focusing. In this work, we design and optimize c-Si nanosphere metalenses working on the magnetic dipole and quadrupole resonances of the c-Si nanoparticle. Resonant optical response of c-Si nanostructures is simulated by the multipole decomposition method along with the zero-order Born approximation. Limitations of this approach are investigated. The obtained results of optimization are verified by simulation via the T-matrix method

Evolutionary and genetic algorithms for design of metadevices working on electric dipole resonance

All-dielectric nanophotonics is a rapidly growing field of modern science. Metasurfaces and other planar devices based on all-dielectric nanoparticles lead to manage the light propagation at the nanoscale. Impressive effects such as perfect absorption, invisibility, chirality effects, negative refraction, light focusing in the area with size smaller than wavelength, nano-lasing etc - can be achieved with all-dielectric technologies. While it is needed to use more and more complicated designs for solution of modern nanophotonics' currents tasks, non-classical methods of optimization become relevant. For example, to design reconfigurable metalenses with an additional degree of freedom such as polarizability or temperature dependence, evolutionary or genetic algorithms show their high applicability. In this work, we show a new approach to design metalenses with evolutionary and genetic algorithms. © 2020 IOP Publishing Ltd

Machine learning of phase transitions in nonlinear polariton lattices

This is the final version. Available from Nature Research via the DOI in this record. The data that support the findings of this study are available from the corresponding author upon reasonable request.The code for the analysis is available from the corresponding author upon reasonable request.Polaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of their steady state as a function of system parameters, which resemble non-equilibrium phase transitions. Unlike their equilibrium counterparts, these transitions cannot be characterised by conventional statistical physics methods. Here, we study a lattice of square-arranged polariton condensates with nearest-neighbour coupling, and simulate the polarisation (pseudospin) dynamics of the polariton lattice, observing regions with distinct steady-state polarisation patterns. We classify these patterns using machine learning methods and determine the boundaries separating different regions. First, we use unsupervised data mining techniques to sketch the boundaries of phase transitions. We then apply learning by confusion, a neural network-based method for learning labels in a dataset, and extract the polaritonic phase diagram. Our work takes a step towards AI-enabled studies of polaritonic systems.Engineering and Physical Sciences Research Council (EPSRC)Russian Foundation for Basic ResearchNATOIcelandic Research FundIcelandic Research Fun

Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

AUTOMATA PROGRAMS CONSTRUCTION FROM SPECIFICATION WITH AN ANT COLONY OPTIMIZATION ALGORITHM BASED ON MUTATION GRAPH

The procedure of testing traditionally used in software engineering cannot guarantee program correctness; therefore verification is used at the excess requirements to programs reliability. Verification makes it possible to check certain properties of programs in all possible computational states; however, this process is very complex. In the model checking method a model of the program is built (often, manually) and requirements in terms of temporal logic are formulated. Such temporal properties of the model can be checked automatically. The main issue in this framework is the gap between the program and its model. Automata-based programming paradigm gives the possibility to overcome this limitation. In this paradigm, program logic is represented using finite-state machines. The advantage of finite-state machines is that their models can be constructed automatically. The paper deals with the application of mutation-based ant colony optimization algorithm to the problem of finite-state machine construction from their specification, defined by test scenarios and temporal properties. The presented approach has been tested on the elevator doors control problem as well as on randomly generated data. Obtained results show the ant colony algorithm is two-three times faster than the previously used genetic algorithm. The proposed approach can be recommended for inferring control programs for critical systems