Yong Xu

Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network

Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PENN-GMD, a parameter estimation neural network that maps partially observed trajectories to a Gaussian mixture distribution (GMD) over the system parameters. Unlike convent...

First-passage statistics of run-and-tumble particles with directional bias and stochastic trapping

We numerically investigate the first-passage time statistics of active particles in confined one- and two-dimensional geometries, subject to stochastic trapping and directional bias. The dynamics alternate between persistent ballistic runs, trapping phases with power-law distributed waiting times, and stochastic reorie...

Geometry-rectified transport of chiral active particles in a radial ratchet

Structured environments organize active-particle currents by coupling propulsion and orientational dynamics to geometry. Translationally periodic ratchets can produce a rotation-frequency-dependent drift across equivalent unit cells, but the locked position remains spatially degenerate. In this paper, we show that a ra...

Data-driven higher-order moment inverse modeling for jump–diffusion dynamics