19 citations · 19 across the 5 of their papers we have counts for
5 papers
Spatio-temporal modeling and forecasting with Fourier neural operators
Pratik Nag, Andrew Zammit-Mangion, Sumeetpal Singh +1
Spatio-temporal process models are often used for modeling dynamic physical and biological phenomena that evolve across space and time. These phenomena may exhibit environmental he…
WOMBAT v2.S: A Bayesian inversion framework for attributing global CO flux components from multiprocess data
Josh Jacobson, Michael Bertolacci, Andrew Zammit-Mangion +2
Contributions from photosynthesis and other natural components of the carbon cycle present the largest uncertainties in our understanding of carbon dioxide (CO) sources and sin…
Neural Parameter Estimation with Incomplete Data
Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie +1
Advances in artificial intelligence (AI) and deep learning have led to neural networks being used to generate lightning-speed answers to complex science questions, paintings in the…
Spatial Bayesian Neural Networks
Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran +2
interpretable, and well understood models that are routinely employed even though, as is revealed through prior and posterior predictive checks, these can poorly characterise the s…
Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion
Andrew Zammit-Mangion, Noel Cressie, Anita L. Ganesan +2
Atmospheric trace-gas inversion refers to any technique used to predict spatial and temporal fluxes using mole-fraction measurements and atmospheric simulations obtained from compu…