  Generative modeling has drawn much attention in creative and scientific data
generation tasks. Score-based Diffusion Models, a type of generative model that
iteratively learns to denoise data, have shown state-of-the-art results on
tasks such as image generation, multivariate time series forecasting, and
robotic trajectory planning. Using score-based diffusion models, this work
implements a novel generative framework to generate ballistic transfers from
Earth to Mars. We further analyze the model's ability to learn the
characteristics of the original dataset and its ability to produce transfers
that follow the underlying dynamics. Ablation studies were conducted to
determine how model performance varies with model size and trajectory temporal
resolution. In addition, a performance benchmark is designed to assess the
generative model's usefulness for trajectory design, conduct model performance
comparisons, and lay the groundwork for evaluating different generative models
for trajectory design beyond diffusion. The results of this analysis showcase
several useful properties of diffusion models that, when taken together, can
enable a future system for generative trajectory design powered by diffusion
models.
