Physics-Informed Machine Learning Simulator for Wildfire Propagation
This research aims to provide a real-time wildfire spread prediction tool for emergency responders and planners, potentially improving response times and resource allocation.
This work explores replacing the numerical solvers in the WRF-SFIRE wildfire simulator with physics-informed machine learning techniques to achieve real-time wildfire spread prediction. The implementation uses Julia, leveraging its performance and numerical computation libraries.
The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques to solve ODEs and PDEs, in order to transform it into a real-time simulator for wildfire spread prediction. The main programming language used is Julia, a compiled language which offers better perfomance than interpreted ones, providing Just in Time (JIT) compilation with different optimization levels. Moreover, Julia is particularly well suited for numerical computation and for the solution of complex physical models, both considering the syntax and the presence of some specific libraries such as DifferentialEquations.jl and ModellingToolkit.jl.