  Identifying the underlying processes that locally dominate physical
interactions is the key to understanding nonlinear dynamics. Machine-learning
techniques have recently been shown to be highly promising in automating the
search for dominant physics, adding important insights that complement
analytical methods and empirical intuition. Here we apply a fully unsupervised
approach to the search for dominant balance during nonlinear and dispersive
propagation in optical fiber, and show that we can algorithmically identify
dominant interactions in cases of optical wavebreaking, soliton fission,
dispersive wave generation, and Raman soliton emergence. We discuss how
dominant balance manifests both in the temporal and spectral domains as a
function of propagation distance.
