About This Architecture

AI-driven metasurface design pipeline combining tandem neural networks with deep reinforcement learning to automate broadband RCS reduction. An LSTM-based policy network generates 4 base unit topologies (7×7 binary), which are expanded to 8 units via symmetry and rotation, then evaluated by a surrogate forward model predicting S11/S22 phase responses across 8–18 GHz. The agent optimizes a 6×6 metasurface arrangement using GRPO, maximizing RCSR (>10 dB) and bandwidth through end-to-end differentiable training with gradient feedback from loss functions combining phase error and RCS reduction objectives. Fork this diagram to customize frequency bands, unit cell dimensions, reward weights, or LSTM hidden states for your own electromagnetic design automation. The tandem architecture—inverse policy model paired with forward surrogate—eliminates expensive EM simulations during training, enabling rapid exploration of topology and arrangement spaces.

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How can machine learning automate the design of broadband RCS reduction metasurfaces without expensive electromagnetic simulations?

This diagram shows a tandem neural network architecture where an LSTM policy network generates optimal unit cell topologies and 6×6 arrangements, while a surrogate forward model predicts electromagnetic phase responses. Deep reinforcement learning with GRPO optimization maximizes RCSR and bandwidth across 8–18 GHz, using gradient feedback from differentiable loss functions to train both the invers