  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.
