431 citations · 552 across the 8 of their papers we have counts for
8 papers
An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales
Neelesh Rampal, Bryn Ward-Leikis, Yun Sing Koh +7
Machine learning (ML) offers a computationally efficient approach for generating large ensembles of high-resolution climate projections, but deterministic ML methods often smooth f…
A Bayesian Ensemble Projection of Climate Change and Technological Impacts on Future Crop Yields
Dan Li, Vassili Kitsios, David Newth +1
This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty forecasting under…
Statistical Dynamics and Subgrid Modelling of Turbulence: From Isotropic to Inhomogeneous
Jorgen S. Frederiksen, Vassili Kitsios, Terence J. O'Kane
Turbulence is the most important, ubiquitous, and difficult problem of classical physics. Feynman viewed it as essentially unsolved, without a rigorous mathematical basis to descri…
Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs
He Zhao, Vassili Kitsios, Terence J. O'Kane +1
We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data.Vector autoregressive (VAR) models have been time-tested…
Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
Edwin V. Bonilla, Pantelis Elinas, He Zhao +3
Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian appro…
Outer scaling of self-similar adverse-pressure-gradient turbulent boundary layers
Atsushi Sekimoto, Vassili Kitsios, Callum Atkinson +1
The prediction of turbulent boundary layer (TBL) flow over a convex surface as in aircraft wings or gas turbine blades is a challenging problem. Finding a universal scaling law of…