43 citations · 58 across the 3 of their papers we have counts for
3 papers
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires
Joel Janek Dabrowski, Daniel Edward Pagendam, James Hilton +5
We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differentia…
Towards Data Assimilation in Level-Set Wildfire Models Using Bayesian Filtering
Joel Janek Dabrowski, Carolyn Huston, James Hilton +2
The level-set method is a prominent approach to modelling the evolution of a fire over time based on a characterised rate of spread. It however does not provide a direct means for…
A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models
Andrew Bolt, Carolyn Huston, Petra Kuhnert +3
Computational simulations of wildfire spread typically employ empirical rate-of-spread calculations under various conditions (such as terrain, fuel type, weather). Small perturbati…