Physics Guided Conditional Diffusion Framework for Generative Inverse Design of Manufacturable Metasurface based Absorbers

2026-05-19 · Source: cs.CV updates on arXiv.org · Field: Science & Research — Engineering & Applied Sciences, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new "Physics Guided Conditional Diffusion Framework" is introduced for the inverse design of manufacturable metasurface-based absorbers. This framework addresses the computational expense of traditional iterative full-wave simulations and the limitations of existing generative models regarding conditional control and fabrication awareness. It incorporates fabrication-aware constraints and a novel conditioning mechanism using feature-wise linear modulation to propagate continuous spectral specifications across the denoising hierarchy, ensuring stable and accurate generation. A pre-trained surrogate EM simulator is integrated into the diffusion training pipeline to embed electromagnetic consistency. The framework successfully generates physically realizable metasurface designs for reflection characteristics across the 2 to 18 GHz frequency range, achieving an average spectral mean squared error of 0.0006 and a band alignment accuracy of 0.958. It also enables structured multimodal generation, producing geometrically distinct yet spectrally consistent designs in approximately 30 seconds, a significant improvement over conventional methods that can take months. Experimental measurements validate its efficiency.

Key takeaway

For research scientists developing advanced electromagnetic devices, this framework offers a critical shift in inverse design methodology. You can now generate manufacturable metasurface designs with precise spectral specifications in seconds, rather than months. This efficiency, coupled with the ability to explore multimodal geometric solutions, allows you to rapidly iterate and optimize designs for applications in the 2 to 18 GHz range, significantly accelerating your development cycles and experimental validation.

Key insights

Physics-guided diffusion models can rapidly generate manufacturable metasurface designs with high spectral accuracy and multimodal diversity.

Principles

• Integrate physics simulators for EM consistency.
• Incorporate fabrication constraints early.
• Use feature-wise modulation for spectral control.

Method

A conditional diffusion framework integrates fabrication-aware constraints, feature-wise linear modulation for spectral conditioning, and a pre-trained surrogate EM simulator within its training pipeline to generate designs.

In practice

• Design metasurface absorbers for 2-18 GHz.
• Generate multiple spectrally consistent geometries.
• Reduce design time from months to seconds.

Topics

• Metasurface Design
• Inverse Design
• Diffusion Models
• Electromagnetic Simulation
• Generative AI
• Fabrication Constraints

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.