MLJC presented the following research at AAAI-MLPS Symposium 2021 on February 2021. Please find the proceedings below or in the official website: https://sites.google.com/view/aaai-mlps/proceedings?authuser=0
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 replacingits core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques tosolve 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 Justin Time (JIT) compilation with different optimization levels. Moreover, Julia is particularlywell suited for numerical computation and for the solution ofcomplex physical models, both considering the syntax and thepresence of specific libraries such as DifferentialEquations.jl and ModelingToolkit.jl.