43 citations · 77 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution
Joel Janek Dabrowski, Daniel Edward Pagendam
We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks. The approach is more scalable to large data than Markov Ch…
The effect of biologically mediated decay rates on modelling soil carbon sequestration in agricultural settings
Mohammad Javad Davoudabadi, Daniel Pagendam, Christopher Drovandi +2
Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the most active component of the soil organic carbon (SOC) that regulates bio-geochemical processes in ter…