首页|Inverse design of nonlinear phononic crystal configurations based on multi-label classification learning neural networks

Inverse design of nonlinear phononic crystal configurations based on multi-label classification learning neural networks

扫码查看
原文链接
  • 万方数据
  • 维普
Phononic crystals,as artificial composite materials,have sparked significant interest due to their novel characteristics that emerge upon the introduction of nonlinearity.Among these properties,second-harmonic features exhibit potential applications in acoustic frequency conversion,non-reciprocal wave propagation,and non-destructive testing.Precisely manipulating the harmonic band structure presents a major challenge in the design of nonlinear phononic crystals.Tradi-tional design approaches based on parameter adjustments to meet specific application requirements are inefficient and often yield suboptimal performance.Therefore,this paper develops a design methodology using Softmax logistic regression and multi-label classification learning to inversely design the material distribution of nonlinear phononic crystals by exploiting information from harmonic transmission spectra.The results demonstrate that the neural network-based inverse design method can effectively tailor nonlinear phononic crystals with desired functionalities.This work establishes a mapping relationship between the band structure and the material distribution within phononic crystals,providing valuable insights into the inverse design of metamaterials.

multi-label classification learningnonlinear phononic crystalsinverse design

黄坤琦、林懿然、赖耘、刘晓宙

展开 >

Key Laboratory of Modern Acoustics,Institute of Acoustics,Nanjing University,Nanjing 210093,China

School of Physics,Collaborative Innovation Center of Advanced Microstructures,Nanjing University,Nanjing 210093,China

State Key Laboratory of Acoustics,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190,China

National Key Research and Development Program of ChinaState Key Program of the National Natural Science of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaState Key Laboratory of Acoustics,Chinese Academy of SciencesFund from the Key Laboratory of Underwater Acoustic Environment,Chinese Academy of Sciences

2020YFA021140011834008121741921217418811974176SKLA202410SSHJ-KFKT-1701

2024

10.1088/1674-1056/ad6b85

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(10)