Physics-Guided and Physics-Explainable Recurrent Neural Network for Time Dynamics in Optical Resonances

TL;DR

This paper introduces a physics-guided recurrent neural network that accurately predicts the time response of optical resonances using minimal data, combining physical models and observational data for broad applicability.

Contribution

The novel model integrates physics guidance into RNNs and employs a multi-fidelity training framework, enabling precise time dynamics prediction with limited high-fidelity data.

Findings

Accurately forecasts resonance time responses with only 7% of full data length.

Successfully applies to diverse resonances like dielectric metasurfaces and graphene plasmonics.

Demonstrates improved analysis speed and physical interpretability in optical resonance studies.

Abstract

Understanding the time evolution of physical systems is crucial to revealing fundamental characteristics that are hidden in frequency domain. In optical science, high-quality resonance cavities and enhanced interactions with matters are at the heart of modern quantum technologies. However, capturing their time dynamics in real-world scenarios suffers from long data acquisition and low analysis accuracy due to slow convergence and limited time window. Here, we report a physics-guided and physics-explainable recurrent neural network to precisely forecast the time-domain response of resonance features with the shortest acquired input sequence being 7\% of full length, and to infer corresponding resonance frequencies. The model is trained in a two-step multi-fidelity framework for high-accuracy forecast, where the first step is based on a large amount of low-fidelity physical-model-generated synthetic data and second step involves a small set of high-fidelity application-oriented observational data. Through both simulations and experiments, we demonstrate that the model is universally applicable to a wide range of resonances, including dielectric metasurfaces, graphene plasmonics, and ultrastrongly coupled Landau polaritons, where our model accurately captures small signal features and learns essential physical quantities. The demonstrated machine learning algorithm offers a new way to accelerate the exploration of physical phenomena and the design of devices under resonance-enhanced light-matter interaction.