Details
| Original language | English |
|---|---|
| Pages (from-to) | 547-576 |
| Number of pages | 30 |
| Journal | International Journal of Mechanics and Materials in Design |
| Volume | 21 |
| Issue number | 3 |
| Early online date | 17 Apr 2025 |
| Publication status | Published - Jun 2025 |
Abstract
The development of phononic crystals provides a possible solution for the precise control of acoustic/elastic waves. Designing phononic crystals with a target characteristic has become a research hotspot in recent years. Nevertheless, the precision with which the acoustic and mechanical waves can be altered remains a major challenge for existing inverse design methods. The rapidly growing machine learning methods revolutionize the design of these materials. As an important branch of machine learning, reinforcement learning is being attempted to solve mechanical problems more intelligently through the interaction of environment and agent. In this paper, we adopt machine learning to successfully design 2D phononic crystals with expected band structure. We firstly applied the meshless generalized finite difference method in solving the dispersion equation for a periodic structure. Then, in order to widen the first-order bandgap width over a desired frequency range, we employ the reinforcement learning algorithm modified by particle swarm optimization to effectively estimate the shape parameters. The parallel technology saves computational costs remains independent of the initial state and target, in addition to being effective and stable. This improved reinforcement learning based interaction design scheme can easily accommodate several other reverse engineering problems.
Keywords
- Generalized finite difference method, Inverse design, Parallel technology, Particle swarm optimization, Phononic crystal, Reinforcement learning
ASJC Scopus subject areas
- Materials Science(all)
- General Materials Science
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
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In: International Journal of Mechanics and Materials in Design, Vol. 21, No. 3, 06.2025, p. 547-576.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Intelligent inverse design of phononic crystals based on machine learning coupled with localized collocation meshless method
AU - Chu, Wenhui
AU - Fu, Zhuojia
AU - Nanthakumar, S. S.
AU - Xu, Wenzhi
AU - Zhuang, Xiaoying
N1 - Publisher Copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2025.
PY - 2025/6
Y1 - 2025/6
N2 - The development of phononic crystals provides a possible solution for the precise control of acoustic/elastic waves. Designing phononic crystals with a target characteristic has become a research hotspot in recent years. Nevertheless, the precision with which the acoustic and mechanical waves can be altered remains a major challenge for existing inverse design methods. The rapidly growing machine learning methods revolutionize the design of these materials. As an important branch of machine learning, reinforcement learning is being attempted to solve mechanical problems more intelligently through the interaction of environment and agent. In this paper, we adopt machine learning to successfully design 2D phononic crystals with expected band structure. We firstly applied the meshless generalized finite difference method in solving the dispersion equation for a periodic structure. Then, in order to widen the first-order bandgap width over a desired frequency range, we employ the reinforcement learning algorithm modified by particle swarm optimization to effectively estimate the shape parameters. The parallel technology saves computational costs remains independent of the initial state and target, in addition to being effective and stable. This improved reinforcement learning based interaction design scheme can easily accommodate several other reverse engineering problems.
AB - The development of phononic crystals provides a possible solution for the precise control of acoustic/elastic waves. Designing phononic crystals with a target characteristic has become a research hotspot in recent years. Nevertheless, the precision with which the acoustic and mechanical waves can be altered remains a major challenge for existing inverse design methods. The rapidly growing machine learning methods revolutionize the design of these materials. As an important branch of machine learning, reinforcement learning is being attempted to solve mechanical problems more intelligently through the interaction of environment and agent. In this paper, we adopt machine learning to successfully design 2D phononic crystals with expected band structure. We firstly applied the meshless generalized finite difference method in solving the dispersion equation for a periodic structure. Then, in order to widen the first-order bandgap width over a desired frequency range, we employ the reinforcement learning algorithm modified by particle swarm optimization to effectively estimate the shape parameters. The parallel technology saves computational costs remains independent of the initial state and target, in addition to being effective and stable. This improved reinforcement learning based interaction design scheme can easily accommodate several other reverse engineering problems.
KW - Generalized finite difference method
KW - Inverse design
KW - Parallel technology
KW - Particle swarm optimization
KW - Phononic crystal
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=105002737213&partnerID=8YFLogxK
U2 - 10.1007/s10999-025-09749-5
DO - 10.1007/s10999-025-09749-5
M3 - Article
AN - SCOPUS:105002737213
VL - 21
SP - 547
EP - 576
JO - International Journal of Mechanics and Materials in Design
JF - International Journal of Mechanics and Materials in Design
SN - 1569-1713
IS - 3
ER -