Reinforcement learning applied to metamaterial design.

Area of Science:

• Acoustics
• Computational Physics
• Machine Learning

Background:

• Acoustic scattering from objects is a significant challenge in various fields.
• Optimizing designs to minimize scattering often requires complex simulations and computations.
• Traditional optimization methods can be computationally intensive and may not explore the full design space effectively.

Purpose of the Study:

• To develop and evaluate a semi-analytical method for suppressing acoustic scattering using reinforcement learning (RL).
• To enable an RL agent to control design parameters of cylindrical scatterers for reduced acoustic scattering.
• To compare the performance of RL-based designs against state-of-the-art optimization algorithms.

Main Methods:

• A reinforcement learning agent was trained to adjust design parameters (position, radius) of cylindrical scatterers.
• The RL agent utilized gradients of the total scattering cross section (TSCS) and state information.
• Double deep Q-learning network and deep deterministic policy gradient algorithms were employed.
• The agent received rewards based on minimizing the root mean square of TSCS across wavenumbers.

Main Results:

• The RL agent successfully learned to perturbatively adjust design parameters to minimize acoustic scattering.
• Discovered designs demonstrated significant suppression of acoustic scattering.
• RL-based optimization outperformed the fmincon algorithm in achieving low scattering.

Conclusions:

• Reinforcement learning offers a powerful and efficient approach for optimizing designs to suppress acoustic scattering.
• The proposed semi-analytical RL method provides a novel way to tackle complex scattering problems.
• This work highlights the potential of RL in advanced acoustic engineering and material design.