MAE Colloquium: Ryne Beeson (Princeton)

About this event

Spacecraft Trajectory Design and Flight with Machine Learning

Low thrust propulsion is now a ubiquitous choice of propulsion system for a wide range of space missions, including in chaotic and complex dynamical environments, such as the Earth-Moon system. A central difficulty of designing for low thrust is the global search problem at high cadence, which requires a balance of modeling fidelity, continuation approaches, and often highly experienced mission designers. A second difficult arises in the operation of the mission where unplanned thruster outage events are likely to occur, and autonomous re-planning is not yet demonstrated. In this seminar we will review the progress of research and use of machine learning in space flight design, and onboard guidance, navigation, and control. We show how enabling generative model formulations and architectures should be chosen based on the navigation problem of interest and then demonstrate recent results for low thrust mission design and onboard guidance in three-body systems.

Bio: Ryne Beeson is an assistant professor of mechanical and aerospace engineering at Princeton University. He received his degrees (B.S., M.S., and Ph.D.) in aerospace engineering and an M.S. in mathematics from the University of Illinois at Urbana-Champaign, where he was also recognized with the Department of Aerospace Engineering’s Kenneth Lee Herrick Memorial Award for outstanding Ph.D. research. He was a senior scientist for CU Aerospace L.L.C. in Champaign, Illinois, where he led several NASA SBIR/STTR award efforts to develop astrodynamics software, including the multibody trajectory optimization software (pydylan), which continues to be advanced with his group at Princeton. He is a recipient of an AFOSR Young Investigator Program award and has been recognized by the International Society of Information Fusion with the 2026 Young Investigator Award. He is a member of the AIAA Guidance Navigation & Control Committee and AAS Technical Committee, and member of the Society for Industrial and Applied Mathematics. His research group works on problems in spacecraft trajectory optimization, dynamical systems theory in space flight, generative machine learning applied to space flight preliminary design and navigation, uncertainty realism and prediction, data assimilation for high dimensional systems, and space policy.