57 citations · 100 across the 3 of their papers we have counts for
3 papers
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems
Dan MacKinlay, Russell Tsuchida, Dan Pagendam +1
Efficient inference in high-dimensional models is a central challenge in machine learning. We introduce the Gaussian Ensemble Belief Propagation (GEnBP) algorithm, which combines t…
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…
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Makoto Takamoto, Timothy Praditia, Raphael Leiteritz +4
Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Sc…