A multi-time Markov renewal theory on partially ordered lattices is developed, yielding stratified inverse-renewal limits that are Gaussian on single-coordinate cells and non-Gaussian minima on interfaces, plus exact-time local theorems and Markovian augmentation criteria.
Springer, Cham
4 Pith papers cite this work, alongside 173 external citations. Polarity classification is still indexing.
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Hybrid simulation and non-Euclidean elasticity theory demonstrate that clathrin coats develop adaptive rigidity and memory during growth, producing flat, stalled, or closed outcomes through two energy-landscape gates and matching experiments without fitted parameters.
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
citing papers explorer
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Multi-time Markov renewal chains and stratified renewal theorems
A multi-time Markov renewal theory on partially ordered lattices is developed, yielding stratified inverse-renewal limits that are Gaussian on single-coordinate cells and non-Gaussian minima on interfaces, plus exact-time local theorems and Markovian augmentation criteria.
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Pathway variability, coat stiffening and mechanical adaptation during clathrin-mediated endocytosis
Hybrid simulation and non-Euclidean elasticity theory demonstrate that clathrin coats develop adaptive rigidity and memory during growth, producing flat, stalled, or closed outcomes through two energy-landscape gates and matching experiments without fitted parameters.
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Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
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Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.