TY - JOUR

T1 - Machine-learning-driven on-demand design of phononic beams

AU - He, Liangshu

AU - Guo, HW

AU - Jin, Yabin

AU - Zhuang, XY

AU - Rabczuk, Timon

AU - Li, Yan

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China (Grant No. 11902223), the Shanghai Pujiang Program (Grant No. 19PJ1410100), the Program for Professors of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, the Fundamental Research Funds for the Central Universities, and Shanghai Municipal Peak Discipline Program (Grant No. 2019010106).

PY - 2022/1

Y1 - 2022/1

N2 - 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.

AB - 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.

KW - phononic crystals

KW - elastic metamaterials

KW - topological insulators

KW - machine learning

KW - reinforcement learning

KW - DEEP NEURAL-NETWORKS

KW - INVERSE DESIGN

KW - PHASE

KW - OPTIMIZATION

KW - CRYSTALS

UR - http://www.scopus.com/inward/record.url?scp=85120159392&partnerID=8YFLogxK

U2 - 10.1007/s11433-021-1787-x

DO - 10.1007/s11433-021-1787-x

M3 - Article

VL - 65

JO - Science China: Physics, Mechanics and Astronomy

JF - Science China: Physics, Mechanics and Astronomy

SN - 1674-7348

IS - 1

M1 - 214612

ER -