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Metamaterial design method and related equipment based on Bayesian collaborative optimization algorithm

A design method and collaborative optimization technology, applied in the field of metamaterials, can solve the problems of manpower and time-consuming, lack of design methods, low efficiency, etc., and achieve the effect of reducing the number of times, saving computing resources and simulation time, and quickly designing

Active Publication Date: 2022-03-29
TSINGHUA UNIV +1
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

At present, the design method of metamaterials is mainly based on manual design, that is, the geometric parameters of the material are designed through the designer's experience and intuition, and it is necessary to manually adjust the geometric parameters for experiments, which requires a lot of manpower and time, and the efficiency is extremely low.
The lack of an efficient multi-objective design method for metamaterials severely restricts the design of metamaterials with more diverse structures and wider applicability

Method used

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  • Metamaterial design method and related equipment based on Bayesian collaborative optimization algorithm
  • Metamaterial design method and related equipment based on Bayesian collaborative optimization algorithm
  • Metamaterial design method and related equipment based on Bayesian collaborative optimization algorithm

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Embodiment 1

[0066] figure 2 It is a flow chart of the metamaterial design method (namely, the metamaterial design method based on the Bayesian collaborative optimization algorithm) provided by Embodiment 1 of the present invention. The metamaterial design method is applied to computer equipment. The metamaterial design method determines the geometric parameters corresponding to the multiple structural primitives through a Bayesian collaborative optimization algorithm according to the target electromagnetic response of the multiple structural primitives constituting the metamaterial.

[0067] Such as figure 2 As shown, the metamaterial design method includes:

[0068] 201. Obtain the working frequency band of the metamaterial The target electromagnetic response set of K structural primitives Z={z 1 ,...,z K} and error threshold set ε={ε 1 ,...,ε K},in

[0069] The target electromagnetic responses in the target electromagnetic response set Z are in one-to-one correspondence wit...

Embodiment 2

[0147] image 3 It is a structural diagram of the metamaterial design device (namely, the metamaterial design device based on the Bayesian collaborative optimization algorithm) provided by Embodiment 2 of the present invention. The metamaterial design device 30 is applied to computer equipment. The metamaterial design device 30 determines the geometric parameters corresponding to the multiple structural primitives through a Bayesian collaborative optimization algorithm according to the target electromagnetic responses of the multiple structural primitives constituting the metamaterial.

[0148] Such as image 3 As shown, the metamaterial design device 30 may include an acquisition module 301, a selection module 302, a first calculation module 303, a modeling module 304, a second calculation module 305, a third calculation module 306, a first judgment module 307, adding Module 308 , second judgment module 309 , output module 310 .

[0149] Obtaining module 301, configured to...

Embodiment 3

[0226] This embodiment provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above embodiment of the metamaterial design method based on the Bayesian collaborative optimization algorithm are implemented, for example figure 2 201-210 shown. Alternatively, when the computer program is executed by the processor, the functions of the modules in the above-mentioned device embodiments are realized, for example image 3 Modules 301-310 in.

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Abstract

The invention provides a metamaterial design method and related equipment based on a Bayesian collaborative optimization algorithm. The method transforms the metamaterial design problem into an optimization problem of a black box function, transforms the complex functional response of the metamaterial into two simple response functions, uses a Gaussian process model to model the two simple response functions, and uses Bayesian collaborative optimization. The algorithm solves the target design. The present invention can realize the rapid design of metamaterials.

Description

technical field [0001] The invention relates to the technical field of metamaterials, in particular to a metamaterial design method based on a Bayesian collaborative optimization algorithm and related equipment. Background technique [0002] In recent years, with the development of material science, metamaterials have attracted widespread attention from scholars at home and abroad. A metamaterial is a man-made material with special electromagnetic properties formed by arranging a large number of structural units in a specific way. Its electromagnetic properties are not determined by the constituent materials, but by the geometric structure of the structural units. Under a certain design, metamaterials can manipulate electromagnetic waves in a new way, and then create a variety of unusual electromagnetic properties, such as negative refraction, phase full photo, super lens, etc. Because the extraordinary electromagnetic properties of metamaterials are not available in natura...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20
CPCG06F30/20
Inventor 邓柯杨洋季春霖
Owner TSINGHUA UNIV
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