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Physics-informed recurrent neural network for time dynamics in optical resonances

Nature Computational Science volume 2, pages 169–178 (2022)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Resonance structures and features are ubiquitous in optical science. However, capturing their time dynamics in real-world scenarios suffers from long data acquisition time and low analysis accuracy due to slow convergence and limited time windows. Here we report a physics-informed recurrent neural network to forecast the time-domain response of optical resonances and infer corresponding resonance frequencies by acquiring a fraction of the sequence as input. The model is trained in a two-step multi-fidelity framework for high-accuracy forecast, using first a large amount of low-fidelity physical-model-generated synthetic data and then a small set of high-fidelity application-specific data. Through simulations and experiments, we demonstrate that the model is applicable to a wide range of resonances, including dielectric metasurfaces, graphene plasmonics and ultra-strongly coupled Landau polaritons, where our model captures small signal features and learns physical quantities. The demonstrated machine-learning algorithm can help to accelerate the exploration of physical phenomena and device design under resonance-enhanced light–matter interaction.

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Fig. 1: Cascaded GRU networks and two-step multi-fidelity training approach.
Fig. 2: Model demonstration for dielectric metasurfaces.
Fig. 3: Model generalization to graphene plasmonics and Landau polaritons.
Fig. 4: Experimental verification of the GRU model in Landau polaritons.

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Data availability

The source data of all figures in both main text and Supplementary Information are available at https://github.com/GaoUtahLab/Cascaded_GRU_Networks. The Zenodo version is available at ref. 45. Source data are provided with this paper.

Code availability

The code for the models in all three demonstrations of optical resonances and that support the plots within this paper and other findings of this study is available at https://github.com/GaoUtahLab/Cascaded_GRU_Networks. The Zenodo version is available at ref. 45.

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Acknowledgements

J.F. and W.G. thank the University of Utah start-up fund for support. X.L. acknowledges support from the Caltech Postdoctoral Prize Fellowship and the IQIM. C.Y. acknowledges support from grants NSF-2047176 and NSF-2008144.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA

    Yingheng Tang, Jichao Fan, Cunxi Yu & Weilu Gao

  2. School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

    Yingheng Tang & Minghao Qi

  3. Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA, USA

    Xinwei Li

  4. Institute of Artificial Intelligence, Peking University, Beijing, China

    Jianzhu Ma

  5. Beijing Institute of General Artificial Intelligence, Beijing, China

    Jianzhu Ma

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Contributions

C.Y. and W.G. conceived the idea and designed the project. Y.T. performed the modeling and calculations with the help of J.F., J.M., M.Q., C.Y. and W.G. X.L. helped with the analysis of the Landau polariton data. Y.T. and W.G. wrote the manuscript. All authors discussed the manuscript and provided feedback.

Corresponding authors

Correspondence to Cunxi Yu or Weilu Gao.

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The authors declare no competing interests.

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Nature Computational Science thanks Andrey Baydin, Bowen Zheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and Tables 1–3.

Source data

Source Data Fig. 2

Numerical data for plots in Fig. 2.

Source Data Fig. 3

Numerical data for plots in Fig. 3.

Source Data Fig. 4

Numerical data for plots in Fig. 4.

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Tang, Y., Fan, J., Li, X. et al. Physics-informed recurrent neural network for time dynamics in optical resonances. Nat Comput Sci 2, 169–178 (2022). https://doi.org/10.1038/s43588-022-00215-2

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