7 papers
Likelihood-informed dimension reduction across tempered Bayesian posteriors
Arne Bouillon, Oliver R. A. Dunbar
Scientific computer simulations cannot represent all scales in realistic applications. To bridge this model-data gap, parameters are injected into models and constrained with noisy…
The Ensemble Kalman Inversion Race
Rebecca Gjini, Matthias Morzfeld, Oliver R. A. Dunbar +1
Ensemble Kalman methods were initially developed to solve nonlinear data assimilation problems in oceanography, but are now popular in applications far beyond their original use ca…
Nesterov Acceleration for Ensemble Kalman Inversion and Variants
Sydney Vernon, Eviatar Bach, Oliver R. A. Dunbar
Ensemble Kalman inversion (EKI) is a derivative-free, particle-based optimization method for solving inverse problems. It can be shown that EKI approximates a gradient flow, which…
Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion
Yifei Guan, Pedram Hassanzadeh, Tapio Schneider +4
Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and…
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features
Oliver R. A. Dunbar, Nicholas H. Nelsen, Maya Mutic
Randomized algorithms exploit stochasticity to reduce computational complexity. One important example is random feature regression (RFR) that accelerates Gaussian process regressio…
Models for information propagation on graphs
Oliver R. A. Dunbar, Charles M. Elliott, Lisa Maria Kreusser
We propose and unify classes of different models for information propagation over graphs. In a first class, propagation is modelled as a wave which emanates from a set of \emph{kno…