7 papers
Neural optimization of the most probable paths of 3D active Brownian particles
Bin Zheng, Zhongqiang Xiong, Changhao Li +7
We develop a variational neural-network framework to determine the most probable path (MPP) of a 3D active Brownian particle (ABP) by directly minimizing the Onsager-Machlup integr…
Geometric formulation of state-dependent Langevin dynamics using scalar free energy
Kento Yasuda, Zhongqiang Xiong, Zhanglin Hou +3
Stochastic dynamics with state-dependent diffusion are widely used for Brownian motion in confined, anisotropic, and hydrodynamically coupled systems. The conventional Langevin for…
Demon's variational principle for informational active matter
Kento Yasuda, Kenta Ishimoto, Shigeyuki Komura
The interplay between information, dissipation, and control is reshaping our understanding of thermodynamics in feedback-regulated systems. We develop the informational Onsager-Mac…
Covariant Onsager and Onsager-Machlup principles for active and inertial dynamics
Kento Yasuda, Bin Zheng, Zhongqiang Xiong +5
The Onsager principle provides a variational route to the phenomenological equations of dissipative dynamics through the minimization of the Rayleighian. We develop a covariant for…
Most probable path and invariant sets in noise-induced transition to turbulence
Yoshiki Hiruta, Kento Yasuda, Kenta Ishimoto
Turbulence transition often arises from a subcritical transition between bistable states characterized by invariant sets of deterministic dynamical systems, and such transitions ca…
Thermally driven two-sphere microswimmer with internal feedback control
Jun Li, Ziluo Zhang, Zhanglin Hou +4
We discuss the locomotion of a thermally driven elastic two-sphere microswimmer with internal feedback control that is realized by the position-dependent friction coefficients. In…