34 citations · 34 across the 1 of their papers we have counts for
5 papers
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles
Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper
The estimation of model parameters with uncertainties from observed data is a ubiquitous inverse problem in science and engineering. In this paper, we suggest an inexpensive and ea…
Ensembling geophysical models with Bayesian Neural Networks
Ushnish Sengupta, Matt Amos, J. Scott Hosking +3
Ensembles of geophysical models improve projection accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bay…
A data-driven kinematic model of a ducted premixed flame
Hans Yu, Matthew P. Juniper, Luca Magri
Reduced-order models of flame dynamics can be used to predict and mitigate the emergence of thermoacoustic oscillations in the design of gas turbine and rocket engines. This proces…
Sensitivity of the Rayleigh criterion in thermoacoustics
Luca Magri, Matthew P. Juniper, Jonas P. Moeck
Thermoacoustic instabilities are one of the most challenging problems faced by gas turbine and rocket motor manufacturers. The key instability mechanism is described by the {\it Ra…
Combined State and Parameter Estimation in Level-Set Methods
Hans Yu, Matthew P. Juniper, Luca Magri
Reduced-order models based on level-set methods are widely used tools to qualitatively capture and track the nonlinear dynamics of an interface. The aim of this paper is to develop…