Amortized Global Search for Efficient Preliminary Trajectory Design with
  Deep Generative Models
Anjian Li Amlan Sinha Ryne Beeson
Preliminary trajectory design is a global search problem that seeks multiple
qualitatively different solutions to a trajectory optimization problem. Due to
its high dimensionality and non-convexity, and the frequent adjustment of
problem parameters, the global search becomes computationally demanding. In
this paper, we exploit the clustering structure in the solutions and propose an
amortized global search (AmorGS) framework. We use deep generative models to
predict trajectory solutions that share similar structures with previously
solved problems, which accelerates the global search for unseen parameter
values. Our method is evaluated using De Jong's 5th function and a low-thrust
circular restricted three-body problem.