About

Ryne Beeson is an Assistant Professor in Mechanical and Aerospace Engineering at Princeton University. He received his BS, MS, and PhD degrees in Aerospace Engineering from the University of Illinois at Urbana-Champaign, as well as an MS in Mathematics from the same institution. Prior to joining the faculty at Princeton University in August 2021, he was a Senior Scientist at CU Aerospace L.L.C. His professional background combines advanced aerospace engineering education with practical experience in aerospace research and development. The Beeson Group at Princeton focuses on optimal low-thrust spacecraft trajectories in multiple gravitational environments and robust low-thrust trajectory optimization, reflecting Professor Beeson's research interests and expertise.

Research topics

• Computer Science

Selected publications

• A Variational Pseudo-Observation Guided Nudged Particle Filter

  ArXiv.org · 2026-03-17

  articleOpen accessSenior author

  Nonlinear filtering with standard PF methods requires mitigative techniques to quell weight degeneracy, such as resampling. This is especially true in high-dimensional systems with sparse observations. Unfortunately, such techniques are also fragile when applied to systems with exceedingly rare events. Nonlinear systems with these properties can be assimilated effectively with a control-based PF method known as the nPF, but this method has a high computational cost burden. In this work, we aim to retain this strength of the nudged method while reducing the computational cost by introducing a variational method into the algorithm that acts as a continuous pseudo-observation path. By maintaining a PF representation, the resulting algorithm continues to capture an approximation of the filtering distribution, while reducing computational runtime and improving robustness to the "rare" event of switching phases. Preliminary testing of the new approach is demonstrated on a stochastic variant of the nonlinear and chaotic L63 model, which is used as a surrogate for mimicking "rare" events. The new approach helps to overcome difficulties in applying the nPF for realistic problems and performs favorably with respect to a standard PF with a higher number of particles.

  PublisherOA PDF

• Gradient-Informed Monte Carlo Fine-Tuning of Diffusion Models for Low-Thrust Trajectory Design

  2026-01-08

  articleSenior author

  Preliminary mission design of low‑thrust spacecraft trajectories in the Circular Restricted Three‑Body Problem is a global search characterized by a complex objective landscape and numerous local minima. Formulating the problem as sampling from an unnormalized distribution supported on neighborhoods of locally optimal solutions, provides the opportunity to deploy Markov chain Monte Carlo methods and generative machine learning. In this work, we extend our previous self-supervised diffusion model fine-tuning framework to employ gradient‑informed Markov chain Monte Carlo. We compare two algorithms - the Metropolis‑Adjusted Langevin Algorithm and Hamiltonian Monte Carlo - both initialized from a distribution learned by a diffusion model. Derivatives of an objective function that balances fuel consumption, time of flight and constraint violations are computed analytically using state transition matrices. We show that incorporating the gradient drift term accelerates mixing and improves convergence of the Markov chain for a multi-revolution transfer in the Saturn-Titan system. Among the evaluated methods, MALA provides the best trade-off between performance and computational cost. Starting from samples generated by a baseline diffusion model trained on a related transfer, MALA explicitly targets Pareto-optimal solutions. Compared to a random walk Metropolis algorithm, it increases the feasibility rate from $17.34\%$ to $63.01\%$ and produces a denser, more diverse coverage of the Pareto front. By fine-tuning a diffusion model on the generated samples and associated reward values with reward-weighted likelihood maximization, we learn the global solution structure of the problem and eliminate the need for a tedious separate data generation phase.

  PublisherDOI

• A Variational Pseudo-Observation Guided Nudged Particle Filter

  arXiv (Cornell University) · 2026-03-17

  preprintOpen accessSenior author

  Nonlinear filtering with standard PF methods requires mitigative techniques to quell weight degeneracy, such as resampling. This is especially true in high-dimensional systems with sparse observations. Unfortunately, such techniques are also fragile when applied to systems with exceedingly rare events. Nonlinear systems with these properties can be assimilated effectively with a control-based PF method known as the nPF, but this method has a high computational cost burden. In this work, we aim to retain this strength of the nudged method while reducing the computational cost by introducing a variational method into the algorithm that acts as a continuous pseudo-observation path. By maintaining a PF representation, the resulting algorithm continues to capture an approximation of the filtering distribution, while reducing computational runtime and improving robustness to the "rare" event of switching phases. Preliminary testing of the new approach is demonstrated on a stochastic variant of the nonlinear and chaotic L63 model, which is used as a surrogate for mimicking "rare" events. The new approach helps to overcome difficulties in applying the nPF for realistic problems and performs favorably with respect to a standard PF with a higher number of particles.

  PublisherDOI

• Cislunar Environmental Outcomes: A Framework for Development and Evaluation of Long-Term Cislunar Orbital Debris Mitigation Policies

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Statistical Analysis of the Role of Invariant Manifolds on Robust Trajectories

  Journal of Guidance Control and Dynamics · 2025-06-10 · 1 citations

  preprintOpen accessSenior author

  As low-thrust space missions grow in prevalence, it is becoming increasingly important to design low-thrust trajectories with robustness against unforeseen thruster outages or missed thrust events. Accounting for such anomalies is particularly important in chaotic multibody systems, such as the cislunar realm, where pertinent dynamical structures constrain the dynamical flow. Yet it remains unclear how these dynamical structures influence robust trajectory design. This paper provides the first comprehensive statistical comparison between nonrobust and robust trajectories in relation to the invariant manifolds of resonant orbits in a circular restricted three-body problem. For both the nonrobust and robust solution categories, the optimal subset exhibits stronger alignment with the invariant manifolds, whereas the broader feasible set can sometimes deviate significantly. On average, the robust optimal trajectories shadow the invariant manifolds as closely as the nonrobust optimal trajectories and, in some instances, exhibit even stronger alignment than their nonrobust counterparts. By maintaining proximity to these invariant manifolds, the robust low-thrust solutions are able to efficiently leverage the global dynamical flow to achieve optimality even under operational uncertainties.

  PublisherOA PDFDOI

• Constraint-Aware Diffusion Models for Trajectory Optimization

  Lecture notes in computer science · 2025-08-25 · 2 citations

  book-chapterSenior author
  PublisherDOI

• A Cylindrical Distribution for Uncertainty Representation at Equilibria of the Circular Restricted Three-Body Problem

  2025-07-07

  article1st authorCorresponding

  Space situational awareness (SSA) relies on accurate and efficient uncertainty realism and propagation (UR&P). In the cislunar domain, there is growing interest in the use of orbits near the collinear libration points, which are relative equilibria in the simplified circular restricted three-body model. These equilibria are hyperbolic and therefore possess dynamical structures in phase space that separate the flow and present difficulties for UR&P. In this paper, we build on prior efforts to define a canonical distribution on a cylindrical space given by the application of normal form theory at a collinear equilibria. The canonical distribution is constructed to have components with disjoint support on the phase space, each of which properly represents the characteristic dynamics for the specific subset of phase space. This is achieved by a product of a multivariate folded normal distribution and conditional normal and von Mises distributions. Comparisons to a regularized distribution, the generalized Bernoulli-Gauss-von Mises, are made.

  PublisherDOI

• Addressing gaps in cislunar orbital debris mitigation governance frameworks via norms of behavior

  Space Policy · 2025-10-01

  articleSenior author
  PublisherDOI

• Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlo

  ArXiv.org · 2025-10-02

  preprintOpen accessSenior author

  Global search and optimization of long-duration, low-thrust spacecraft trajectories with the indirect method is challenging due to a complex solution space and the difficulty of generating good initial guesses for the costate variables. This is particularly true in multibody environments. Given data that reveals a partial Pareto optimal front, it is desirable to find a flexible manner in which the Pareto front can be completed and fronts for related trajectory problems can be found. In this work we use conditional diffusion models to represent the distribution of candidate optimal trajectory solutions. We then introduce into this framework the novel approach of using Markov Chain Monte Carlo algorithms with self-supervised fine-tuning to achieve the aforementioned goals. Specifically, a random walk Metropolis algorithm is employed to propose new data that can be used to fine-tune the diffusion model using a reward-weighted training based on efficient evaluations of constraint violations and missions objective functions. The framework removes the need for separate focused and often tedious data generation phases. Numerical experiments are presented for two problems demonstrating the ability to improve sample quality and explicitly target Pareto optimality based on the theory of Markov chains. The first problem does so for a transfer in the Jupiter-Europa circular restricted three-body problem, where the MCMC approach completes a partial Pareto front. The second problem demonstrates how a dense and superior Pareto front can be generated by the MCMC self-supervised fine-tuning method for a Saturn-Titan transfer starting from the Jupiter-Europa case versus a separate dedicated global search.

  PublisherOA PDFDOI

• Bi-Level Optimal Control Framework For Missed-Thrust-Design With First-Order Bounds On Maximum Missed-Thrust-Duration

  arXiv (Cornell University) · 2025-12-22

  preprintOpen accessSenior author

  In this paper, we present a bi-level optimal control framework for designing low-thrust spacecraft trajectories with robustness against missed-thrust-events. The upper-level (UL) problem generates a nominal trajectory assuming full control authority, while each lower-level (LL) problem computes the optimal recovery maneuver following a missed-thrust-event along the nominal solution. Under suitable regularity conditions ensuring uniqueness and smoothness of the LL response, the hierarchy admits a single-level reformulation by embedding the LL first-order optimality conditions within the UL constraints. We further establish a robustness certificate, which provides an upper bound on the maximum admissible missed-thrust-duration for which the structural assumptions remain valid for the LL problem. The bound depends explicitly on precomputable dynamical quantities along the nominal solution, enabling rapid evaluation over large ensembles without iterative solves. Numerical experiments show that while the certificate identifies when modeling assumptions are valid, it does not fully characterize recoverability after missed-thrust-events. A finite-horizon controllability-energy analysis is therefore used to interpret recovery beyond the theoretical bounds. Collectively, these results provide a deterministic, certifiable approach for incorporating robustness directly into trajectory design, replacing post-hoc margin allocation techniques with formal guarantees.

  PublisherDOI

Frequent coauthors

• N. Sri Namachchivaya

  University of Waterloo

  47 shared

• Nicolas Perkowski

  44 shared

• Peter W. Sauer

  University of Illinois Urbana-Champaign

  38 shared

• Richard Fujimoto

  Georgia Institute of Technology

  36 shared

• Erik Blasch

  36 shared

• Aradhya Biswas

  Georgia Institute of Technology

  36 shared

• Luca Carlone

  36 shared

• Shashank Shekhar

  36 shared

Labs

• Beeson GroupPI

Awards & honors

• Young Investigator Program award