323 citations · 2k across the 63 of their papers we have counts for
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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…
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…
Towards Implementing Responsible AI
Conrad Sanderson, Qinghua Lu, David Douglas +3
As the deployment of artificial intelligence (AI) is changing many fields and industries, there are concerns about AI systems making decisions and recommendations without adequatel…