Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
Read the original on arXiv Machine Learning →The paper presents a transfer‑learning framework that combines homotopy in mission parameters with Markov chain Monte Carlo (MCMC) to generate training data more efficiently for indirect low‑thrust trajectory optimization. By reformulating the multiobjective problem as sampling from an unnormalized target distribution in costate space, the authors compare three MCMC algorithms on a planar multi‑revolution transfer in the circular restricted three‑body problem, finding that gradient‑based variants offer the best trade‑off between sample quality and computational cost. The resulting MCMC samples are used to fine‑tune a diffusion model conditioned on the mass parameter, enabling the model to learn a global representation of the solution distribution and produce new high‑quality solutions, ultimately yielding 40 % more feasible solutions and a superior Pareto front compared to a state‑of‑the‑art indirect approach.
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