CANDID finds faint companion around star in interferometric data in the OIFITS format. It allows systematically searching for faint companions in OIFITS data, and if not found, estimates the detection limit. The tool is based on model fitting and Chi2 minimization, with a grid for the starting points of the companion position. It ensures all positions are explored by estimating a-posteriori if the grid is dense enough, and provides an estimate of the optimum grid density.

GRINN (Gravity Informed Neural Network) solves the coupled set of time-dependent partial differential equations describing the evolution of self-gravitating flows in one, two, and three spatial dimensions. It is based on physics informed neural networks (PINNs), which are mesh-free and offer a fundamentally different approach to solving such partial differential equations. GRINN has solved for the evolution of self-gravitating, small-amplitude perturbations and long-wavelength perturbations and, when modeling 3D astrophysical flows, provides accuracy on par with finite difference (FD) codes with an improvement in computational speed.