Titlebook: Structural Modeling of Metamaterials; Vladimir I. Erofeev,Igor S. Pavlov Book 2021 Springer Nature Switzerland AG 2021 Metamaterials.Struc

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Application of the 2D Models of Media with Dense and Non-dense Packing of the Particles for Solvingescribing the propagation and interaction of longitudinal and transverse waves in a granular medium in the low-frequency approximation with the equations of the classical theory of elasticity, we will consider the problem of parametric identification of the developed models.

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A Cubic Lattice of Spherical Particles, to obtain dynamic equations of a crystalline medium consisting of spherical particles by the method of structural modeling and to establish the relationships between the coefficients of these equations and the microstructure parameters of the material at issue.

BARGE

Book 2021nsidered medium and into performing theoretical estimations of these quantities. This book is useful for researchers working in the fields of solid mechanics, physical acoustics, and condensed matter physics, as well as for graduate and postgraduate students studying mathematical modeling methods..

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A Two-Dimensional Lattice with Non-dense Packing of Particles,possesses two translational and one rotational degrees of freedom. The space between the particles is a massless medium through which the force and coupled interactions are transmitted. In limiting cases, this model degenerates either into a chain of ellipse-shaped particles or into a square lattice

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A Cubic Lattice of Spherical Particles,is chapter is devoted to the elaboration of a three-dimensional model of a crystalline medium consisting of spherical particles with six degrees of freedom. Such a medium is structurally similar to a fullerite crystal with a simple cubic lattice (see Sect.?.). The main objectives of this chapter are

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1869-8433 scribes the propagation and interaction of nonlinear waves i.This book discusses the theoretical foundations of the structural modeling method applied to metamaterials. This method takes into account the parameters of the crystal lattice, the size of the medium particles, as well as their shape and

Constituent

. trains a fully-connected neural network using the cross-entropy method to approximate the policy of a choosing destination physical machine for each migrating virtual machine. We compare . with state-of-the-art heuristic algorithms in the literature, and our results show that . effectively leads