Prof. Feruza Amirkulova Profile

Prof. Feruza Amirkulova

Assistant Professor at San Jose State Univ

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Proceedings Article | 3 October 2022 Presentation + Paper

Generative modeling and reinforcement learning for acoustic lens design

Wei-Ching Wang, Feruza Amirkulova

Proceedings Volume 12195, 121950A (2022) https://doi.org/10.1117/12.2642956

KEYWORDS: Acoustics, Lens design, Optimization (mathematics), Metamaterials, Gallium nitride, Multiple scattering, Scattering, Performance modeling, MATLAB, Algorithm development

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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.

Proceedings Article | 20 August 2020 Poster

Control of acoustic waves using finite acoustic metaclusters

Peter Lai, Feruza Amirkulova, Mawuto Attiogbe

Proceedings Volume 11460, 114602O (2020) https://doi.org/10.1117/12.2568327

KEYWORDS: Acoustics, Scattering, Multiple scattering, Particles, Instrument modeling, Manufacturing, Optimization (mathematics)

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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.

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