  The rapid development of 6G communications using terahertz (THz)
electromagnetic waves has created a demand for highly sensitive THz
nanoresonators capable of detecting these waves. Among the potential
candidates, THz nanogap loop arrays show promising characteristics but require
significant computational resources for accurate simulation. This requirement
arises because their unit cells are 10 times smaller than millimeter
wavelengths, with nanogap regions that are 1,000,000 times smaller. To address
this challenge, we propose a rapid inverse design method using physics-informed
machine learning, employing double deep Q-learning with an analytical model of
the THz nanogap loop array. In about 39 hours on a middle-level personal
computer, our approach identifies the optimal structure through 200,000
iterations, achieving experimental electric field enhancement of 32,000 at 0.2
THz, 300 % stronger than prior results. Our analytical model-based approach
significantly reduces computational resources, offering a practical alternative
to numerical simulation-based inverse design
