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

作　　者：Kunqi Huang Yiran Lin Yun Lai Xiaozhou Liu 黄坤琦;林懿然;赖耘;刘晓宙(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)

机构地区：[1]Key Laboratory of Modern Acoustics,Institute of Acoustics,Nanjing University,Nanjing 210093,China [2]School of Physics,Collaborative Innovation Center of Advanced Microstructures,Nanjing University,Nanjing 210093,China [3]State Key Laboratory of Acoustics,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Chinese Physics B》2024年第10期295-301,共7页中国物理B（英文版）

基　　金：supported by the National Key Research and Development Program of China(Grant No.2020YFA0211400);the State Key Program of the National Natural Science of China(Grant No.11834008);the National Natural Science Foundation of China(Grant Nos.12174192,12174188,and 11974176);the State Key Laboratory of Acoustics,Chinese Academy of Sciences(Grant No.SKLA202410);the Fund from the Key Laboratory of Underwater Acoustic Environment,Chinese Academy of Sciences(Grant No.SSHJ-KFKT-1701).

摘　　要：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.Traditional 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.

关 键 词：multi-label classification learning nonlinear phononic crystals inverse design 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O735[自动化与计算机技术—控制科学与工程]