141 citations · 317 across the 14 of their papers we have counts for
14 papers
Democratizing Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard +22
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In thi…
A transport approach to sequential simulation-based inference
Paul-Baptiste Rubio, Youssef Marzouk, Matthew Parno
We present a new transport-based approach to efficiently perform sequential Bayesian inference of static model parameters. The strategy is based on the extraction of conditional di…
Efficient and Exact Multimarginal Optimal Transport with Pairwise Costs
Bohan Zhou, Matthew Parno
In this paper, we address the numerical solution to the multimarginal optimal transport (MMOT) with pairwise costs. MMOT, as a natural extension from the classical two-marginal opt…
ParticLS: Object-oriented software for discrete element methods and peridynamics
Andrew D. Davis, Brendan A. West, Nathanael J. Frisch +2
ParticLS (\emph{Partic}le \emph{L}evel \emph{S}ets) is a software library that implements the discrete element method (DEM) and meshfree methods. ParticLS tracks the interaction be…
FlakeOut: A Geometric Approach to Remove Wind-Blown Snow from Terrestrial Laser Scans
David Clemens-Sewall, Matthew Parno, Don Perovich +2
Wind-blown snow particles often contaminate Terrestrial Laser Scanning (TLS) data of snow covered terrain. However, common filtering techniques fail to filter wind-blown snow and i…
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty
Ki-Tae Kim, Umberto Villa, Matthew Parno +3
Bayesian inference provides a systematic framework for integration of data with mathematical models to quantify the uncertainty in the solution of the inverse problem. However, the…
Bonded discrete element simulations of sea ice with non-local failure: Applications to Nares Strait
Brendan West, Devin O'Connor, Matthew Parno +2
The discrete element method (DEM) can provide detailed descriptions of sea ice dynamics that explicitly model floes and discontinuities in the ice, which can be challenging to repr…
A Bayesian Approach for Inferring Sea Ice Loads
Matthew Parno, Taylor Hodgdon, Brendan West +2
The Earth's climate is rapidly changing and some of the most drastic changes can be seen in the Arctic, where sea ice extent has diminished considerably in recent years. As the Arc…
Remote measurement of sea ice dynamics with regularized optimal transport
M. D. Parno, B. A. West, A. J. Song +2
As Arctic conditions rapidly change, human activity in the Arctic will continue to increase and so will the need for high-resolution observations of sea ice. While satellite imager…
High dimensional inference for the structural health monitoring of lock gates
Matthew Parno, Devin O'Connor, Matthew Smith
Locks and dams are critical pieces of inland waterways. However, many components of existing locks have been in operation past their designed lifetime. To ensure safe and cost effe…
Improved Workflow for Unsupervised Multiphase Image Segmentation
Brendan A. West, Taylor S. Hodgdon, Matthew D. Parno +1
Quantitative image analysis often depends on accurate classification of pixels through a segmentation process. However, imaging artifacts such as the partial volume effect and sens…
An introduction to sampling via measure transport
Youssef Marzouk, Tarek Moselhy, Matthew Parno +1
We present the fundamentals of a measure transport approach to sampling. The idea is to construct a deterministic coupling---i.e., a transport map---between a complex "target" prob…
A multiscale strategy for Bayesian inference using transport maps
Matthew Parno, Tarek Moselhy, Youssef Marzouk
In many inverse problems, model parameters cannot be precisely determined from observational data. Bayesian inference provides a mechanism for capturing the resulting parameter unc…
Transport map accelerated Markov chain Monte Carlo
Matthew Parno, Youssef Marzouk
We introduce a new framework for efficient sampling from complex probability distributions, using a combination of optimal transport maps and the Metropolis-Hastings rule. The core…