Physics-Informed ML Simulator for Wildfire Propagation
2021-07-29, 13:30–13:40 (UTC), Green

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.

See also: Paper we have written

I am in the second year of the Bachelor Degree in Physics at the University of Turin. I am mainly interested in the theoretical and mathematical aspects of physics. I also conduct research in machine learning, with a particular interest towards the connection between machine learning and physics. Recently I have worked in the field of Scientific Machine Learning using Julia libraries, such as NeuralPDE.jl.

Valerio is a student in Physics (Bachelor) at the University of Turin. He is interested in electronics, embedded systems, computing and signal processing as tools for investigating physics, such as the technology of the detectors employed in high energy physics.

I am an undergraduate physics student at the University of Turin. I have a strong interest in many aspects of physics, ranging from theoretical physics to HEP and cosmology. Currently I am doing my undergraduate thesis about the study of the properties of the hypertriton nuclei with ALICE's data using machine learning techniques. Meanwhile, I am one of the co-founder of MLJC, a student association that focuses on ML research and know-how sharing. My interest are mainly on scientific ML, NLP & NLU, and the theoretical aspects of ML. One of my strongest motivation is to help accelerating pure sciences using ML approaches. I participated in the ProjectX 2020 competition held by the University of Toronto in the UniTo team.