Diffusion limits of cyclic finite-velocity random motions along vector fields
Abstract
Under a scaling of vanishing run times and diverging speeds, cyclic random motions converge to multidimensional diffusions whose drift depends on the switching order, vector-field geometry, and whether motion is flight or gliding.
We investigate diffusion approximations for a class of multivariate inhomogeneous finite-velocity random motions motivated by models of active particles. A particle alternates cyclically among prescribed velocity fields V_1,dots,V_p with random run times and evolves according to either a flight dynamics, consisting of piecewise-linear motion, or a gliding dynamics, in which the particle follows the corresponding velocity flow. Under a Kac-type scaling that couples vanishing run times with diverging particle speed, we prove weak convergence of both processes to multidimensional diffusions. It turns out that the order of successive cyclic motions affects the diffusion limit. In addition to the second-order term generated by fluctuations of the random run times, the limiting drift contains directional derivatives DV_i[V_j] of the underlying vector fields. In Stratonovich form, part of this drift is expressed through their Lie brackets [V_i,V_j]. Thus, the generic non-commutativity of the microscopic motions survives the diffusive scaling and generates a macroscopic drift which depends on the cyclic order. The limiting drift also distinguishes the flight and gliding dynamics, despite their being driven by the same vector fields and run times. Several examples, including run-and-reverse motion and cyclic dynamics generated by multiple vector fields, illustrate how the cyclic switching protocol and the geometry of the underlying velocity fields shape the limiting diffusions.
Community
We evaluated a Cyclic Lie-Bracket Residual Block inspired by Section 2/3 of the paper (replacing standard residual updates with a centered cyclic composition of vector fields to exploit bracket-generated drift).In a dynamics forecasting benchmark (8-seed paired test vs a tuned baseline), the cyclic structure yielded a ~52.8% reduction in MSE ($p=0.0081$). Interestingly, while empirical accuracy improved significantly, small-step order-gap scaling exhibited an exponent of ~2.99 rather than the predicted 2.0, suggesting the performance gain may stem from secondary training dynamics rather than the exact theoretical limit.Code and full benchmark details: Cyclic Lie-Bracket Residual Block
Get this paper in your agent:
hf papers read 2608.22514 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper