mlsanityData to 7 Oct 2026

Towards Photonic Band Diagram Generation with Transformer-Latent Diffusion Models

arXiv 2510.01749 ↗First posted 2 Oct 2025physics.optics cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Photonic crystals enable fine control over light propagation at the nanoscale, and thus play a central role in the development of photonic and quantum technologies. Photonic band diagrams (BDs) are a key tool to investigate light propagation into such inhomogeneous structured materials. However, computing BDs requires solving Maxwell's equations across many configurations, making it numerically expensive, especially when embedded in optimization loops for inverse design techniques, for example. To address this challenge, we introduce the first approach for BD generation based on diffusion models, with the capacity to later generalize and scale to arbitrary three dimensional structures. Our method couples a transformer encoder, which extracts contextual embeddings from the input structure, with a latent diffusion model to generate the corresponding BD. In addition, we provide insights into why transformers and diffusion models are well suited to capture the complex interference and scattering phenomena inherent to photonics, paving the way for new surrogate modeling strategies in this domain.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 36 references that are in the map. First posted on arXiv 2 Oct 2025.

Builds on

6 of 36 references.

References · 6

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2022A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstructioncited by 150
  5. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  6. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model
  2. 2026A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design
  3. 2025Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models
  4. 2025Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media
  5. 2026Generating Symmetric Materials using Latent Flow Matching
  6. 2025Inverse Design of Diffractive Metasurfaces Using Diffusion Models
  7. 2026Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models
  8. 2025Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers