# Branching diffusion representation of semi-linear elliptic PDEs and estimation using Monte Carlo method

## TL;DR

This paper introduces a probabilistic branching diffusion approach to solve semi-linear elliptic PDEs with polynomial non-linearity, enabling solution estimation via Monte Carlo methods without assuming prior existence of solutions.

## Contribution

It extends branching diffusion representations to PDEs with non-linear gradient terms and provides explicit conditions for their validity, advancing probabilistic solution techniques.

## Key findings

- Probabilistic representation for PDE solutions using branching processes.
- Explicit conditions under which the representations are valid.
- Successful Monte Carlo estimation of solutions in multi-dimensional cases.

## Abstract

We study semi-linear elliptic PDEs with polynomial non-linearity and provide a probabilistic representation of their solution using branching diffusion processes. When the non-linearity involves the unknown function but not its derivatives, we extend previous results in the literature by showing that our probabilistic representation provides a solution to the PDE without assuming its existence. In the general case, we derive a new representation of the solution by using marked branching diffusion processes and automatic differentiation formulas to account for the non-linear gradient term. In both cases, we develop new theoretical tools to provide explicit sufficient conditions under which our probabilistic representations hold. As an application, we consider several examples including multi-dimensional semi-linear elliptic PDEs and estimate their solution by using the Monte Carlo method.