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Physics-Informed Machine Learning Simulator for Wildfire Propagation

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arxiv 2012.06825 v1 pith:AOBE7BN4 submitted 2020-12-12 cs.LG

Luca Bottero , Francesco Calisto , Giovanni Graziano , Valerio Pagliarino , Martina Scauda , Sara Tiengo , Simone Azeglio This is my paper · ORCID
classification cs.LG
keywords simulatorjulialanguagelearningmachinenumericalphysics-informedsome
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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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  1. Physics-guided spatiotemporal neural models for fuel density prediction

    cs.LG 2026-07 conditional novelty 4.0 of 10

    Adding physics-guided loss terms to ConvLSTM, AFNONet, and ViViT improves fuel density prediction accuracy and stability over purely data-driven baselines on simulated prescribed-fire data.

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