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 implemented in Julia, in order to transform it into a real-time simulator for wildfire spread prediction.
The study we carried out has the goal to investigate the applicability of the recently developed field of Scientific Machine Learning on climate, wildfire in particular, models. We have outlined some results that tell us that many improvements are needed in order to transform this into a validated product, but also show the big potential of our approach. We need to add further refinements to the implementation in order to carry out a precise time comparison between our approach and the standard numerical solvers, but the results obtained thus far show promising evidence. The encouraging outcome inspires us to continue our work by improving the architectures and possibly employ them in different fields of research. We hope that this line of research will be a small step towards a more effective cohesiveness between Machine Learning and Physical Models in Climate Sciences, and thus further explored by other researchers.