Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media

2025-11-07

AI Summary

To address the high computational cost of iterative optimization and the ill-posedness (non-uniqueness) of inverse electromagnetic design, this paper proposes an end-to-end generative approach based on conditional diffusion models that directly maps target microwave scattering cross-section spectra to structured dielectric geometries. We introduce a feature-linearly modulated 1D U-Net architecture to model the highly nonlinear spectrum-to-geometry mapping, while explicitly capturing solution-space diversity via the stochastic sampling mechanism inherent to diffusion processes—thereby mitigating non-uniqueness. On unseen targets, our method achieves a median relative error below 19% (best case: 1.39%), reduces design time from hours to seconds, and significantly outperforms conventional optimization methods such as CMA-ES. The framework is efficient, diverse in solution generation, and scalable to broader electromagnetic inverse design tasks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

We present a conditional diffusion model for electromagnetic inverse design that generates structured media geometries directly from target differential scattering cross-section profiles, bypassing expensive iterative optimization. Our 1D U-Net architecture with Feature-wise Linear Modulation learns to map desired angular scattering patterns to 2x2 dielectric sphere structure, naturally handling the non-uniqueness of inverse problems by sampling diverse valid designs. Trained on 11,000 simulated metasurfaces, the model achieves median MPE below 19% on unseen targets (best: 1.39%), outperforming CMA-ES evolutionary optimization while reducing design time from hours to seconds. These results demonstrate that employing diffusion models is promising for advancing electromagnetic inverse design research, potentially enabling rapid exploration of complex metasurface architectures and accelerating the development of next-generation photonic and wireless communication systems. The code is publicly available at https://github.com/mikzuker/inverse_design_metasurface_generation.

Problem

• Generates structured media geometries from target scattering profiles
• Handles non-uniqueness of inverse problems by sampling diverse designs
• Reduces electromagnetic design time from hours to seconds

Innovation

• Conditional diffusion model generates geometries from scattering profiles
• 1D U-Net with Feature-wise Linear Modulation handles inverse problem non-uniqueness
• Model reduces design time from hours to seconds while outperforming optimization