Yugen Qi

We propose a physics-informed neural network (PINN) for dynamic cavity-length demodulation of fiber Fabry–Perot (F–P) sensors operating under ultra-sparse eight-channel arrayed-waveguide-grating (AWG) sampling. The F–P interference equation is embedded as a differentiable operator within the loss function, anchoring network optimization to the analytically correct fringe pattern at discrete wavelength channels spaced 0.8 nm apart.