A Variational Lagrangian Framework for Log-Homotopy Particle Flow Filters

TL;DR

This paper introduces a variational Lagrangian framework for log-homotopy particle flow filters, modeling particle transport as an optimal fluid flow guided by principles from physics and quantum mechanics.

Contribution

It formulates the particle flow as an optimal irrotational potential flow using a variational approach, connecting Bayesian filtering with hydrodynamic and quantum analogies.

Findings

Derives Euler–Lagrange equations for optimal flow

Establishes analogy with Madelung's quantum hydrodynamics

Provides a dynamical formulation for particle flow

Abstract

The log-homotopy particle flow filter resolves the Bayesian update by transporting particles along a continuous trajectory in pseudo-time. However, the governing partial differential equation for the flow velocity is fundamentally underdetermined, admitting an infinite family of valid solutions. In this work, we regard the particle flow as the motion of a pressureless inviscid fluid. We define a Lagrangian action based on the kinetic energy of the system, subject to the constraints imposed by the continuity equation and the log-homotopy evolution. By applying the principle of least action, we obtain the Euler--Lagrange equations for the optimal flow, which yields an irrotational potential flow structure. We show that this variational framework yields a coupled Hamilton--Jacobi equation structurally isomorphic to Madelung's hydrodynamic formulation of quantum mechanics. In this analogy, the log-homotopy constraint acts as a generalized quantum potential that generates the force required to guide the probability fluid along the exact Bayesian update path. Finally, we derive the material acceleration of the flow, shifting the formulation from a kinematic to a dynamical description. This perspective could enable the application of higher-order symplectic integrators for improved numerical stability and provide a physics-based metric for adaptive stiffness detection in high-dimensional filtering.