activity
20192023
most citedBayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires

43 citations · 64 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023★ 1 cited

A Neural Emulator for Uncertainty Estimation of Fire Propagation

Andrew Bolt, Conrad Sanderson, Joel Janek Dabrowski +2

Wildfire propagation is a highly stochastic process where small changes in environmental conditions (such as wind speed and direction) can lead to large changes in observed behavio…

cs.LG2022★ 43 cited

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…

cs.LG2022★ 10 cited

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…

cs.AI2020★ 6 cited

Using Unity to Help Solve Intelligence

Tom Ward, Andrew Bolt, Nik Hemmings +10

In the pursuit of artificial general intelligence, our most significant measurement of progress is an agent's ability to achieve goals in a wide range of environments. Existing pla…

cs.AI2019★ 4 cited

Learned human-agent decision-making, communication and joint action in a virtual reality environment

Patrick M. Pilarski, Andrew Butcher, Michael Johanson +3

Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automatio…