2 papers
cs.CE2022
Prior-informed Uncertainty Modelling with Bayesian Polynomial Approximations
Chun Yui Wong, Pranay Seshadri, Andrew B. Duncan +2
Orthogonal polynomial approximations form the foundation to a set of well-established methods for uncertainty quantification known as polynomial chaos. These approximations deliver…
physics.flu-dyn2020
Uncertainty Quantification for Data-driven Turbulence Modelling with Mondrian Forests
Ashley Scillitoe, Pranay Seshadri, Mark Girolami
Data-driven turbulence modelling approaches are gaining increasing interest from the CFD community. However, the introduction of a machine learning (ML) model introduces a new sour…