For linear-rate master equations the generating function admits an exact composition-multiplier representation whose Taylor coefficients on any finite window are obtained from a closed lower-triangular ODE of size 2(N+1), independent of the truncation cap N; the same closure is combined with Strang–
DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia.Journal of Open Research Software, 5(1):15, May 2017
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A MISOCP-SCP two-stage framework enables solar sail station-keeping in eLLOs for at least one year without propellant by leveraging predictable eccentricity vector behavior.
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.
A nonlinear analytical theory derived via asymptotic analysis identifies four dynamical regimes for heterogeneous chemotactic cell collectives and predicts a balanced mixing-localization regime for dendritic and T cell co-migration enabled by strong chemoattractant consumption.
A homotopy-continuation based globalized re-initialization scheme is developed to restore convergence in switched DAE power system simulations where direct post-event solution fails.
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.
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Solving linear-rate ODE hierarchies (like master equations) using closures and operator splitting
For linear-rate master equations the generating function admits an exact composition-multiplier representation whose Taylor coefficients on any finite window are obtained from a closed lower-triangular ODE of size 2(N+1), independent of the truncation cap N; the same closure is combined with Strang–
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Station-Keeping Approach for Extremely Low Lunar Orbits with Solar Sailing
A MISOCP-SCP two-stage framework enables solar sail station-keeping in eLLOs for at least one year without propellant by leveraging predictable eccentricity vector behavior.
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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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A nonlinear theory for chemotactic fronts of mixed populations
A nonlinear analytical theory derived via asymptotic analysis identifies four dynamical regimes for heterogeneous chemotactic cell collectives and predicts a balanced mixing-localization regime for dendritic and T cell co-migration enabled by strong chemoattractant consumption.
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Homotopy-Based Re-Initialization for Switched DAEs in Power System Transient Simulation
A homotopy-continuation based globalized re-initialization scheme is developed to restore convergence in switched DAE power system simulations where direct post-event solution fails.
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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.
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