# Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media

arXiv: https://arxiv.org/abs/2511.05357 · HTML: https://mlsanity.com/paper/2511.05357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 11 of 11)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [Step-by-Step Diffusion: An Elementary Tutorial](https://mlsanity.com/paper/2406.08929/) (2024)
- [Multifunctional Metasurface Design with a Generative Adversarial Network](https://mlsanity.com/paper/1908.04851/) (2019)
- [cmaes: A Simple yet Practical Python Library for CMA-ES](https://mlsanity.com/paper/2402.01373/) (2024)
- [Accelerating gradient-based topology optimization design with dual-model neural networks](https://mlsanity.com/paper/2009.06245/) (2020)
- [Deep learning for size-agnostic inverse design of random-network 3D printed mechanical metamaterials](https://mlsanity.com/paper/2212.12047/) (2022)

## Built on by (most cited first, 1 of 1)

- [DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction](https://mlsanity.com/paper/2607.24785/) (2026)

## Abstract (arXiv)

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.