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
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
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.
KA-CRNN learns continuous SOC-dependent kinetic parameters for cathode-electrolyte decomposition directly from DSC data, reproducing heat-release features across all SOCs for NCA, NM, and NMA cathodes.
Trajectory data resolves structural non-identifiability in lattice random walk diffusion models that count data alone cannot, with analysis of experimental design impacts on practical identifiability.
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.
A tutorial on using StructuralIdentifiability.jl to assess local and global identifiability in ODE models with examples from epidemiology, pharmacokinetics, and systems biology.
citing papers explorer
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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
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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.
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Learning continuous state of charge dependent thermal decomposition kinetics for Li-ion cathodes using Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs)
KA-CRNN learns continuous SOC-dependent kinetic parameters for cathode-electrolyte decomposition directly from DSC data, reproducing heat-release features across all SOCs for NCA, NM, and NMA cathodes.
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When do trajectories matter? Identifiability analysis for stochastic transport phenomena
Trajectory data resolves structural non-identifiability in lattice random walk diffusion models that count data alone cannot, with analysis of experimental design impacts on practical identifiability.
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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.
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A Tutorial on Symbolic Structural Identifiability Analysis of ODE Models in Julia
A tutorial on using StructuralIdentifiability.jl to assess local and global identifiability in ODE models with examples from epidemiology, pharmacokinetics, and systems biology.