Diffusion model-based inverse design for thermal transparency

Author:

Liu Bin1ORCID,Xu Liujun2ORCID,Wang Yixi3ORCID,Huang Jiping3ORCID

Affiliation:

1. Department of Electronic Information and Artificial Intelligence, LeShan Normal University 1 , 614099 LeShan, China

2. Graduate School of China Academy of Engineering Physics 2 , Beijing 100193, China

3. Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory of Micro and Nano Photonic Structures (MOE), Fudan University 3 , 200438 Shanghai, China

Abstract

Generative models in the field of artificial intelligence and their applications and deployment have demonstrated their great strength in the past few years. Of the vast spectrum of generative models, diffusion probabilistic models have proven to be particularly powerful and productive, transforming notions such as text-to-image and text-to-video generation from ideas into practical applications. In our previous works, we proposed a thermal metamaterial-based periodic interparticle interaction mechanism for heat management, with a specific application in thermal transparency. To address the challenging problems associated with the inverse design of thermal metamaterial structures, we employed an autoencoder-based machine learning approach and a reinforcement learning-based approach successfully. In this work, we demonstrate that our particular problems with the inverse design of thermal metamaterial-based periodic lattices for the realization of thermal transparency can also be reframed and efficiently solved by training a generative diffusion probabilistic model that can generate the design parameters corresponding to the desired response. Furthermore, we show that for a specific response, multiple sets of design parameters can be obtained by simply performing multiple inferences with the generative diffusion probabilistic model, enabling us to select the ones that can be more economical to fabricate and implement. Our work is among the first to use a diffusion model for the inverse design of thermal metamaterial-based structures and demonstrates the effectiveness of generating low-dimensional design parameters through a diffusion model.

Funder

National Natural Science Foundation of China

Innovation Program of Shanghai Municipal Education Commission

Publisher

AIP Publishing

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