High-Fidelity Prediction of Perturbed Optical Fields using Fourier Feature Networks

• URL: http://arxiv.org/abs/2508.19751v2
• Date: Tue, 02 Sep 2025 09:02:37 GMT
• Title: High-Fidelity Prediction of Perturbed Optical Fields using Fourier Feature Networks
• Authors: Joshua R. Jandrell, Mitchell A. Cox,
• Abstract summary: We present a novel data-efficient machine learning framework that learns the perturbation-dependent transmission matrix of a multimode fibre.<n>On experimental data from a compressed fibre, our model predicts the output field with a 0.995 complex correlation to the ground truth.<n>This approach provides a general tool for modelling complex optical systems from sparse measurements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the effects of physical perturbations on optical channels is critical for advanced photonic devices, but existing modelling techniques are often computationally intensive or require exhaustive characterisation. We present a novel data-efficient machine learning framework that learns the perturbation-dependent transmission matrix of a multimode fibre. To overcome the challenge of modelling the resulting highly oscillatory functions, we encode the perturbation into a Fourier Feature basis, enabling a compact multi-layer perceptron to learn the mapping with high fidelity. On experimental data from a compressed fibre, our model predicts the output field with a 0.995 complex correlation to the ground truth, improving accuracy by an order of magnitude over standard networks while using 85\% fewer parameters. This approach provides a general tool for modelling complex optical systems from sparse measurements.

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