Matthew P. Juniper

According to our database, Matthew P. Juniper authored at least 16 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning.

Alexandros Kontogiannis, Scott V. Elgersma, Andrew J. Sederman, Matthew P. Juniper

CoRR, 2024

2023

Physics-Informed Compressed Sensing for PC-MRI: An Inverse Navier-Stokes Problem.

Alexandros Kontogiannis, Matthew P. Juniper

IEEE Trans. Image Process., 2023

2022

Physics-informed Deep Learning for Flow Modelling and Aerodynamic Optimization.

Yubiao Sun, Ushnish Sengupta, Matthew P. Juniper

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

2021

Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks.

Ushnish Sengupta, Alexandros Kontogiannis, Matthew P. Juniper

CoRR, 2021

Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning.

Ushnish Sengupta, Günther Waxenegger-Wilfing, Jan Martin, Justin Hardi, Matthew P. Juniper

CoRR, 2021

Online parameter inference for the simulation of a Bunsen flame using heteroscedastic Bayesian neural network ensembles.

Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper

CoRR, 2021

Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models.

Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper

Proceedings of the Computational Science - ICCS 2021, 2021

2020

Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning.

Günther Waxenegger-Wilfing, Ushnish Sengupta, Jan Martin, Wolfgang Armbruster, Justin Hardi, Matthew P. Juniper, Michael Oschwald

CoRR, 2020

Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles.

Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper

CoRR, 2020

Ensembling geophysical models with Bayesian Neural Networks.

Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Combined state and parameter estimation in level-set methods.

Hans Yu, Matthew P. Juniper, Luca Magri

J. Comput. Phys., 2019

2017

Adjoint-based sensitivity analysis of low-order thermoacoustic networks using a wave-based approach.

José G. Aguilar, Luca Magri, Matthew P. Juniper

J. Comput. Phys., 2017

2016

Stability analysis of thermo-acoustic nonlinear eigenproblems in annular combustors. Part II. Uncertainty quantification.

Luca Magri, Michael Bauerheim, Franck Nicoud, Matthew P. Juniper

J. Comput. Phys., 2016

Stability analysis of thermo-acoustic nonlinear eigenproblems in annular combustors. Part I. Sensitivity.

Luca Magri, Michael Bauerheim, Matthew P. Juniper

J. Comput. Phys., 2016

2013

Matrix-free continuation of limit cycles for bifurcation analysis of large thermoacoustic systems.

Iain Waugh, Simon Illingworth, Matthew P. Juniper

J. Comput. Phys., 2013

2012

Adjoint algorithms for the Navier-Stokes equations in the low Mach number limit.

Gary J. Chandler, Matthew P. Juniper, Joseph W. Nichols, Peter J. Schmid

J. Comput. Phys., 2012