A Diffusion Model for Multi-Layered Metasurface Unit Cell Synthesis

Niu, Chen; Phaneuf, Mario; Mojabi, Puyan

doi:10.1109/ojap.2023.3292149

Cited by 8 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The diffusion model starts by noising the input data with several continuous steps until it becomes fully white noise, and then, the denoiser continuously recovers the input data [106,107]. Similar to the VAE, the diffusion model itself can be used for inverse designing metasurfaces [108,109]. Meanwhile, diffusion-GAN is an example where the denoiser of the diffusion model serves as a generator when solving image-classifying problems [110].…”

Section: Generatormentioning

confidence: 99%

Review of Generative Models for the Inverse Design of Nanophotonic Metasurfaces

Moon,

Kang,

Yeo

2023

Appl. Sci. Converg. Technol.

View full text Add to dashboard Cite

Evolving nanotechnologies and further understanding of nanophotonics have recently enabled the control of electromagnetic waves using metasurfaces. Since metasurfaces can provide diverse optical characteristics depending on their geometries, the forward design of metasurfaces conventionally has been employed through an understanding of the physical effects of each geometrical parameter. In contrast, the inverse design approach optimizes the metasurface geometry using computational algorithms. This review discusses recent studies on constructing generative models for the inverse design of nanophotonic metasurfaces. The generative model for inverse design is constructed mainly with three components: an evaluator, a generator, and a criterion. The evaluator, which can be implemented by physical simulators or deep neural networks, determines whether the input metasurface geometry satisfies the target optical characteristics. The generator suggests new possible design candidates that may have optical properties close to the target. The criterion, which includes algorithms based on mathematical optimization and artificial intelligence, manages the operation of a generative model while satisfying the convergence of optimal solutions. Inverse design takes advantage of larger design space for customized applications along with the possibility of investigating new physics, and hence it is expected to improve metasurfaces further with the emerging computational algorithms.

show abstract

Section: Generatormentioning

confidence: 99%

Review of Generative Models for the Inverse Design of Nanophotonic Metasurfaces

Moon,

Kang,

Yeo

2023

Appl. Sci. Converg. Technol.

View full text Add to dashboard Cite

show abstract

Intelligent Architectures for Extreme Event Visualisation

Song,

Pagnucco,

et al. 2024

Arts, Research, Innovation and Society

View full text Add to dashboard Cite

Realistic immersive visualisation can provide a valuable method for studying extreme events and enhancing our understanding of their complexity, underlying dynamics and human impacts. However, existing approaches are often limited by their lack of scalability and incapacity to adapt to diverse scenarios. In this chapter, we present a review of existing methodologies in intelligent visualisation of extreme events, focusing on physical modelling, learning-based simulation and graphic visualisation. We then suggest that various methodologies based on deep learning and, particularly, generative artificial intelligence (AI) can be incorporated into this domain to produce more effective outcomes. Using generative AI, extreme events can be simulated, combining past data with support for users to manipulate a range of environmental factors. This approach enables realistic simulation of diverse hypothetical scenarios. In parallel, generative AI methods can be developed for graphic visualisation components to enhance the efficiency of the system. The integration of generative AI with extreme event modelling presents an exciting opportunity for the research community to rapidly develop a deeper understanding of extreme events, as well as the corresponding preparedness, response and management strategies.

show abstract