mlsanityData to 14 Oct 2026

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

arXiv 2308.03960 ↗First posted 7 Aug 2023cs.LG math.OCcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 11 of its 39 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

11 of 39 references.

References · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2022Is Conditional Generative Modeling all you need for Decision-Making?cited by 516
  5. 2016Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoderscited by 220
  6. 2016Variational Deep Embedding: An Unsupervised and Generative Approach to Clusteringcited by 220
  7. 2018Molecular generative model based on conditional variational autoencoder for de novo molecular designcited by 73
  8. 2019Large Scale Model Predictive Control with Neural Networks and Primal Active Setscited by 29
  9. 2022End-to-End Learning to Warm-Start for Real-Time Quadratic Optimizationcited by 26
  10. 2021CoCo: Online Mixed-Integer Control via Supervised Learningcited by 16
  11. 2023Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Modelscited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 3.

12 papers.

  1. 2024DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimizationcited by 20
  2. 2025Global Search for Optimal Low Thrust Spacecraft Trajectories using Diffusion Models and the Indirect Methodcited by 8
  3. 2025Aligning Diffusion Model with Problem Constraints for Trajectory Optimizationcited by 7
  4. 2024Learning Optimal Control and Dynamical Structure of Global Trajectory Search Problems with Diffusion Modelscited by 6
  5. 2025Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformerscited by 4
  6. 2025Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinementcited by 1
  7. 2025Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlocited by 1
  8. 2025Gradient-Informed Monte Carlo Fine-Tuning of Diffusion Models for Low-Thrust Trajectory Designcited by 1
  9. 2026Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlocited by 1
  10. 2026GLENS: Global Search via Learning from Solver Iterates with Diffusion Modelscited by 1
  11. 2025A Prefixed Patch Time Series Transformer for Two-Point Boundary Value Problems in Three-Body Problemscited by 0
  12. 2026Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iteratescited by 0