  We present a differentiable simulation architecture for articulated
rigid-body dynamics that enables the augmentation of analytical models with
neural networks at any point of the computation. Through gradient-based
optimization, identification of the simulation parameters and network weights
is performed efficiently in preliminary experiments on a real-world dataset and
in sim2sim transfer applications, while poor local optima are overcome through
a random search approach.
