Learning highly oscillatory optical fields with Fourier feature networks
Joshua R. Jandrell, Mitchell Arij Cox · Optics Letters · 2026
Accurately modelling physical perturbations in optical systems is critical for photonic device design, yet existing characterization methods are often computationally prohibitive. We introduce a data-efficient machine learning framework that learns the perturbation-dependent transmission matrix of a multimode fiber. To circumvent the spectral bias that prevents standard neural networks from resolving high-frequency phase changes, we explicitly encode perturbations into a Fourier Feature basis. This approach enables a compact multi-layer perceptron to learn the mapping from sparse training data with high fidelity. Using experimental data from a mechanically deformed fiber, our model achieves a 0.996 complex correlation with the ground truth, improving phase accuracy by an order of magnitude over standard networks while using significantly fewer parameters. This framework transforms the transmission matrix into a continuous, differentiable "digital twin" of the system, providing a robust tool for characterizing complex media in rapidly evolving environments.