Data-driven model discovery of ideal four-wave mixing in nonlinear fibre optics

We show using numerical simulations that data driven discovery using sparse regression can be used to extract the governing differential equation model of ideal four-wave mixing in a nonlinear Schrodinger equation optical fibre system. Specifically, we consider the evolution of a strong single frequency pump interacting with two frequency detuned sidebands where the dynamics are governed by a reduced Hamiltonian system describing pump-sideband coupling. Based only on generated dynamical data from this system, sparse regression successfully recovers the underlying physical model, fully capturing the dynamical landscape on both sides of the system separatrix. We also discuss how analysing an ensemble over different initial conditions allows us to reliably identify the governing model in the presence of noise. These results extend the use of data driven discovery to ideal four-wave mixing in nonlinear Schrodinger equation systems.

Automated summary

Using sparse regression, we successfully recovered the governing differential equation model of ideal four-wave mixing in a nonlinear Schrodinger equation optical fiber system from dynamical data. Analysis of ensemble data allowed us to reliably identify the governing model in the presence of noise.