Physics-Informed Machine Learning Simulator for Wildfire Propagation
- URL: http://arxiv.org/abs/2012.06825v1
- Date: Sat, 12 Dec 2020 14:13:26 GMT
- Title: Physics-Informed Machine Learning Simulator for Wildfire Propagation
- Authors: Luca Bottero, Francesco Calisto, Giovanni Graziano, Valerio
Pagliarino, Martina Scauda, Sara Tiengo and Simone Azeglio
- Abstract summary: This work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator.
The main programming language used is Julia, a compiled language which offers better perfomance than interpreted ones.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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