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Preliminary trajectory design is a global search problem that seeks multiplequalitatively different solutions to a trajectory optimization problem. Due toits high dimensionality and non-convexity, and the frequent adjustment ofproblem parameters, the global search becomes computationally demanding. Inthis paper, we exploit the clustering structure in the solutions and propose anamortized global search (AmorGS) framework. We use deep generative models topredict trajectory solutions that share similar structures with previouslysolved problems, which accelerates the global search for unseen parametervalues. Our method is evaluated using De Jong's 5th function and a low-thrustcircular restricted three-body problem.

Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Models