Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments
cs.LG, physics.app-ph, physics.comp-ph
Submitted: 2026-08-30
Updated: 2026-08-30
Code: https://github.com/mikzuker/inverse_design_metasurface_generation
License: http://creativecommons.org/licenses/by/4.0/
The gist: Future wireless systems are expected to transform the surrounding space from a passive propagation medium into a smart electromagnetic environment, where engineered surfaces control wave propagation,
Terminology
Abstract
Future wireless systems are expected to transform the surrounding space from a passive propagation medium into a smart electromagnetic environment, where engineered surfaces control wave propagation, support wireless sensing, and create programmable electromagnetic fingerprints. A key challenge in realizing this vision is the inverse design of metasurfaces for tailored electromagnetic propagation. While forward analysis evaluates the response of a known geometry, the inverse task starts from a prescribed scattering signature and seeks a physically realizable structure that produces it. This inverse task is inherently nonlinear and often high-dimensional, while candidate solutions may be non-unique and provide no direct indication of practical realizability. Here, we introduce a conditional diffusion framework for inverse design of dielectric resonator metasurfaces from target angular scattering patterns. Trained on T-matrix simulated geometry-response pairs, the model learns a conditional distribution of geometries instead of a deterministic mapping, enabling multiple candidate designs for the ill-posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA-ES optimization (4.1% after 10 h) while requiring only about one minute for after-training inference. The model also produces lower error distributions than deterministic neural baselines for out-of-distribution spectra, highlighting the potential of diffusion models for efficient metasurface design.
Sources
- Accelerating gradient-based topology optimization design with dual-model neural networks
- Step-by-Step Diffusion: An Elementary Tutorial
- Improved Denoising Diffusion Probabilistic Models
- Denoising Diffusion Probabilistic Models
- FiLM: Visual Reasoning with a General Conditioning Layer
- The CMA Evolution Strategy: A Tutorial
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