Mitchell Cox

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.

Open dataset initiative for machine learningbased linearization in analog radio over fiber systems