  To accurately reproduce measurements from the real world, simulators need to
have an adequate model of the physical system and require the parameters of the
model be identified.
  We address the latter problem of estimating parameters through a Bayesian
inference approach that approximates a posterior distribution over simulation
parameters given real sensor measurements. By extending the commonly used
Gaussian likelihood model for trajectories via the multiple-shooting
formulation, our chosen particle-based inference algorithm Stein Variational
Gradient Descent is able to identify highly nonlinear, underactuated systems.
We leverage GPU code generation and differentiable simulation to evaluate the
likelihood and its gradient for many particles in parallel.
  Our algorithm infers non-parametric distributions over simulation parameters
more accurately than comparable baselines and handles constraints over
parameters efficiently through gradient-based optimization. We evaluate
estimation performance on several physical experiments. On an underactuated
mechanism where a 7-DOF robot arm excites an object with an unknown mass
configuration, we demonstrate how our inference technique can identify
symmetries between the parameters and provide highly accurate predictions.
  Project website: https://uscresl.github.io/prob-diff-sim
