论文标题
物理信息的机器学习模拟器用于野火传播
Physics-Informed Machine Learning Simulator for Wildfire Propagation
论文作者
论文摘要
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.所使用的主要编程语言是朱莉娅（Julia），这是一种比解释性语言提供更好的性能，提供了及时的（JIT）汇编，并具有不同的优化级别。此外，考虑到语法和某些特定库的存在，例如差异化和modellingtoolkit.jl，朱莉娅特别适合数值计算和复杂物理模型的解决方案。
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.
