A new stochastic differential dynamic programming method optimizes coupled trajectory design and orbit determination under partial observability, producing navigation-aware solutions with lower fuel consumption than deterministic local optimization in examples like the circular restricted three-body
DifferentialEquations.jl—A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
Trajectory data resolves structural non-identifiability in parameter estimation for stochastic diffusion models that arises with count data alone.
Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.
Generative conditional flow matching deep learning estimates kinetic parameters for itaconic acid production simulations more accurately and robustly than direct deep learning, matching nonlinear regression across operating conditions and scales.
citing papers explorer
-
Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
A new stochastic differential dynamic programming method optimizes coupled trajectory design and orbit determination under partial observability, producing navigation-aware solutions with lower fuel consumption than deterministic local optimization in examples like the circular restricted three-body
-
When do trajectories matter? Identifiability analysis for stochastic transport phenomena
Trajectory data resolves structural non-identifiability in parameter estimation for stochastic diffusion models that arises with count data alone.
-
Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.
-
Deep Learning for Model Calibration in Simulation of Itaconic Acid Production
Generative conditional flow matching deep learning estimates kinetic parameters for itaconic acid production simulations more accurately and robustly than direct deep learning, matching nonlinear regression across operating conditions and scales.