Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
This work introduces a transfer learning framework that uses homotopy and Markov Chain Monte Carlo (MCMC) to efficiently generate training data for diffusion models in indirect trajectory optimization. It combines parameter homotopy with MCMC to learn a global representation of solution distributions, allowing the models to quickly generate high-quality solutions across a continuous range of mission parameters.