期刊
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
卷 65, 期 1, 页码 -出版社
SCIENCE PRESS
DOI: 10.1007/s11433-021-1787-x
关键词
phononic crystals; elastic metamaterials; topological insulators; machine learning; reinforcement learning
资金
- National Natural Science Foundation of China [11902223]
- Shanghai Pujiang Program [19PJ1410100]
- Program for Professors of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning
- Fundamental Research Funds for the Central Universities
- Shanghai Municipal Peak Discipline Program [2019010106]
This study demonstrates how machine learning can be used to study phononic crystal beams through two inverse design schemes. It provides a method for inverse design of structural parameters to maximize the bandgap width and uses a neural network to solve the training difficulty problem and achieve inverse structure design with targeted topological properties.
The development of phononic crystals, especially their interaction with topological insulators, allows exploration of the anomalous properties of acoustic/elastic waves for various applications. However, rapidly and inversely exploring the geometry of specific targets remains a major challenge. In this work, we show how machine learning can address this challenge by studying phononic crystal beams using two different inverse design schemes. We first develop the theory of phononic beams using the transfer matrix method. Then, we use the reinforcement learning algorithm to effectively and inversely design the structural parameters to maximize the bandgap width. Furthermore, we employ the tandem-architecture neural network to solve the training-difficulty problem caused by inconsistent data and complete the task of inverse structure design with the targeted topological properties. The two inverse-design schemes have different adaptabilities, and both are characterized by high efficiency and stability. This work provides deep insights into the combination of machine learning, topological property, and phononic crystals and offers a reliable platform for rapidly and inversely designing complex material and structure properties.
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