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The topological simulation of acoustic waves has induced unconventional propagation characteristics, thereby offering extensive application potential in the field of acoustics. In this paper, we propose a machine learning-assisted method for the inverse design of acoustic wave topological edge states and demonstrate its practical applicability. Leveraging the predictions from a trained artificial neural network algorithm, the design of wide-bandwidth topological insulators is achieved, with simulation results indicating an approximately 2.8-fold enlargement of the single-cell topological bandgap. Further investigation into their wide-bandwidth topological transport properties is conducted. Additionally, two distinct functional acoustic routing devices are devised. Superior performance of the wide-bandwidth acoustic topological devices has been verified through simulation experiments. This approach provides an efficient and viable avenue for the design and optimization of acoustic devices, with the potential to enhance the management and control efficiency of acoustic signal propagation. [ABSTRACT FROM AUTHOR]

The study of optical topological insulators (PTIs) has revealed intriguing optical properties that diversify the ways in which light can be manipulated, offering significant potential for a wide range of applications. This paper presents a machine learning (ML)-based approach for the reverse design of optical PTIs. Using finite element methods, the paper addresses the challenge of computing the band structure of a dual-band model, enabling the construction of a dataset suitable for ML training. With the goal of maximizing dual-band bandgaps, the study employs the random forest algorithm to predict target parameters and further designs topological edge states. Leveraging these boundary state patterns, two different optical PTI beam splitters are devised, and their transmission coefficients and losses are computed. The results demonstrate that optical devices designed using topological boundary states exhibit enhanced stability and robustness. This approach offers a reliable solution for applications in fields such as optical communication and optical sensing. [ABSTRACT FROM AUTHOR]