Bidirectional Fourier-Enhanced Deep Operator Network for Spatio-Temporal Propagation in Multi-Mode Fibers (2512.15474v1)

Abstract: Ultrashort-pulse propagation in graded-index multimode fibers is a highly nonlinear phenomenon driven by several physical processes. Although conventional numerical solvers can reproduce this behavior with high fidelity, their computational cost limits real-time prediction, rapid parameter exploration, experimental feedback, and especially inverse retrieval of input fields from measured outputs. In this work, we introduce an operator learning framework that learns both the forward and inverse propagation operators within a single unified architecture. By combining spectral filters for spatio-temporal representations with Fourier-embedded conditioning on physical parameters, the model functions as a fast surrogate capable of accurately predicting complex field evolution on previously unseen cases. To our knowledge, this represents one of the first demonstrations of a bidirectional operator-learning framework applied to ultrashort-pulse multimode fiber propagation. The resulting architecture enables orders-of-magnitude speedup over numerical solvers, paving the way for real-time beam diagnostics, data-driven design of complex input fields, and closed-loop spatio-temporal control. Moreover, the same framework can potentially be applied to a wide variety of wave systems exhibiting analogous nonlinear and dispersive effects in optics and beyond.