  We present our new experimental and theoretical framework which combines a
broadband superluminescent diode (SLED/SLD) with fast learning algorithms to
provide speed and accuracy improvements for the optimization of 1D optical
dipole potentials, here generated with a Digital Micromirror Device (DMD). To
characterize the setup and potential speckle patterns arising from coherence,
we compare the superluminescent diode to a single-mode laser by investigating
interference properties. We employ Machine Learning (ML) tools to train a
physics-inspired model acting as a digital twin of the optical system
predicting the behavior of the optical apparatus including all its
imperfections. Implementing an iterative algorithm based on Iterative Learning
Control (ILC) we optimize optical potentials an order of magnitude faster than
heuristic optimization methods. We compare iterative model-based offline
optimization and experimental feedback-based online optimization. Our methods
provide a new route to fast optimization of optical potentials which is
relevant for the dynamical manipulation of ultracold gases.
