Conditioning two diffusion processes with respect to their first-encounter properties

TL;DR

This paper develops a framework for conditioning two independent diffusion processes on their first-encounter properties, using entropy optimization, and applies it to Brownian, Ornstein-Uhlenbeck, and tanh-drift processes, linking to stochastic control theory.

Contribution

It introduces a comprehensive method to condition diffusion processes on encounter events using entropy optimization, extending to various process types and connecting with stochastic control.

Findings

Explicit conditioned trajectories for Brownian, Ornstein-Uhlenbeck, and tanh-drift processes.

A unified entropy-based approach for conditioning on first-encounter properties.

Connection established between conditioning and stochastic control via large deviations.

Abstract

We consider two independent identical diffusion processes that annihilate upon meeting in order to study their conditioning with respect to their first-encounter properties. For the case of finite horizon $T<+\infty$, the maximum conditioning consists in imposing the probability $P^*(x,y,T ) $ that the two particles are surviving at positions $x$ and $y$ at time $T$, as well as the probability $\gamma^*(z,t) $ of annihilation at position $z$ at the intermediate times $t \in [0,T]$. The adaptation to various conditioning constraints that are less-detailed than these full distributions is analyzed via the optimization of the appropriate relative entropy with respect to the unconditioned processes. For the case of infinite horizon $T =+\infty$, the maximum conditioning consists in imposing the first-encounter probability $\gamma^*(z,t) $ at position $z$ at all finite times $t \in [0,+\infty[$, whose normalization $[1- S^*(\infty )]$ determines the conditioned probability $S^*(\infty ) \in [0,1]$ of forever-survival. This general framework is then applied to the explicit cases where the unconditioned processes are respectively two Brownian motions, two Ornstein-Uhlenbeck processes, or two tanh-drift processes, in order to generate stochastic trajectories satisfying various types of conditioning constraints. Finally, the link with the stochastic control theory is described via the optimization of the dynamical large deviations at Level 2.5 in the presence of the conditioning constraints that one wishes to impose.