92 citations · 171 across the 12 of their papers we have counts for
14 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 cas…
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…
Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar +1
Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for…
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…
Ensemble-Based Experimental Design for Targeting Data Acquisition to Inform Climate Models
Oliver R. A. Dunbar, Michael F. Howland, Tapio Schneider +1
Data required to calibrate uncertain GCM parameterizations are often only available in limited regions or time periods, for example, observational data from field campaigns, or dat…
Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions
Tapio Schneider, Oliver R. A. Dunbar, Jinlong Wu +8
Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts…
Parameter uncertainty quantification in an idealized GCM with a seasonal cycle
Michael F. Howland, Oliver R. A. Dunbar, Tapio Schneider
Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning o…
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM
Oliver R. A. Dunbar, Alfredo Garbuno-Inigo, Tapio Schneider +1
Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of unc…
Binary recovery via phase field regularization for first traveltime tomography
Oliver R. A. Dunbar, Charles M. Elliott
We propose a double obstacle phase field methodology for binary recovery of the slowness function of an Eikonal equation found in first traveltime tomography. We treat the inverse…
Phase field modelling of surfactants in multi-phase flow
Oliver R. A. Dunbar, Kei Fong Lam, Bjorn Stinner
A diffuse interface model for surfactants in multi-phase flow with three or more fluids is derived. A system of Cahn-Hilliard equations is coupled with a Navier-Stokes system and a…
Reconciling Bayesian and perimeter regularization for binary inversion
Oliver R. A. Dunbar, Matthew M. Dunlop, Charles M. Elliott +2
A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization via the use of the total variation. On…