This research develops data-driven methods for metamaterial design using generative modeling and reinforcement learning (RL). Previously, both generative modeling1 and RL2 showed exciting results for acoustic cloak design. We want to generalize both frameworks for acoustic lens design. The proposed 2D-Global Optimization Networks (2D-GLOnets) maximize the root mean square (RMS) of the absolute pressure at the focal point at discrete wavenumber values to enable acoustic lens design. The 2D-GLOnets1 are adapted with a reparametrization technique that constrains the scatterers’ positions into a feasible region. The pressure amplitude can converge to optimal values faster because of the gradients computed analytically from a multiple scattering solver.3 The loss function with respect to the weights is utilized to update the generator’s weights. In addition, Deep Deterministic Policy Gradient (DDPG) algorithm is applied to the acoustic lens design. DDPG controls the positions of the cylinders and assigns rewards based on the absolute RMS pressure amplitude at the focal point. The reward function assigns a higher value to the state of absolute pressure amplitude. As the agent iteratively completes episodes, the reward is maximized. The agent searches for the configuration of the scatterers that produce the enhanced focusing effect. The numerical results are presented for both models considering uniform configurations of scatterers with a varying number of scatterers and wavenumbers.
In this talk, a novel method to model finite metaclusters that can steer the energy of an incident wave preferentially toward a given direction will be presented. This design is realized by solving an inverse multiple scattering problem for selecting a desired energy distribution of scattered waves. The incident wave energy can be redirected toward a desired direction using a 2D metacluster configuration with a finite number of fluid cylinders embedded in a homogeneous fluid medium. For a faster implementation of the method, we consider a small cylindrical particle limit which corresponds to low frequency scattering. The required mechanical properties of fluid scatterers are defined by T-matrix components obtained by solving a linear system of equations. A major challenge in implementing and applying our computational model to the design of metacluster devices is to ensure that the scatterers remain manufacturable using available conventional materials. These metaclusters are designed by minimizing the relative error between given and computed scattering patterns and by using advanced optimization algorithms and deep learning. Steering the incident acoustic wave energy is realized by designing simple physically implementable configurations consisting of only three or more scatterers.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.