Publication Date
Fall 2022
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Mechanical Engineering
Advisor
Feruza A. Amirkulova
Subject Areas
Mechanical engineering
Abstract
Metamaterials are engineered composites that can exhibit acoustic, electromagnetic, elasto-dynamic and mechanical properties that are not found in natural materials Due to the complexity of the target objective functions, it is difficult to find the globally optimized solutions in the inverse design of metameterials. This thesis proposes and outlines two model, a gradient-based optimization method combined with generative networks (2D-GLOnets) and a reinforcement learning (RL) model, that can find the optimized metamaterial structures across a wide range of parameters. By perturbing the positions of each cylindrical scatterer in a planar configuration, 2D-GLOnets and the RL model with Deep Deterministic Policy Gradients (DDPG) are developed to design 2D (two-dimensional) broadband acoustic cloaking devices at the desired range of wavenumbers. Both models were implemented using PyTorch, Python libraries, and MATLAB engine. The numerical results are presented and compared with the optimal values produced by fmincon algorithms to verify the validation of our approaches. Our results indicated that both methods reduced the total scattering cross section by 17% to 90% compared to the initial conditions.
Recommended Citation
Zhuo, Linwei, "Acoustic Cloak Design Using Generative Modeling and Reinforcement Learning" (2022). Master's Theses. 5355.
DOI: https://doi.org/10.31979/etd.ze2x-ygsh
https://scholarworks.sjsu.edu/etd_theses/5355
