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Journal Articles
- 2023
[j7]Alexandros Kontogiannis
, Matthew P. Juniper:
Physics-Informed Compressed Sensing for PC-MRI: An Inverse Navier-Stokes Problem. IEEE Trans. Image Process. 32: 281-294 (2023)- 2019
- [j6]Hans Yu, Matthew P. Juniper, Luca Magri:
Combined state and parameter estimation in level-set methods. J. Comput. Phys. 399 (2019) - 2017
- [j5]José G. Aguilar, Luca Magri, Matthew P. Juniper:
Adjoint-based sensitivity analysis of low-order thermoacoustic networks using a wave-based approach. J. Comput. Phys. 341: 163-181 (2017) - 2016
- [j4]Luca Magri, Michael Bauerheim, Matthew P. Juniper:
Stability analysis of thermo-acoustic nonlinear eigenproblems in annular combustors. Part I. Sensitivity. J. Comput. Phys. 325: 395-410 (2016) - [j3]Luca Magri, Michael Bauerheim, Franck Nicoud, Matthew P. Juniper:
Stability analysis of thermo-acoustic nonlinear eigenproblems in annular combustors. Part II. Uncertainty quantification. J. Comput. Phys. 325: 411-421 (2016) - 2013
- [j2]Iain Waugh, Simon Illingworth, Matthew P. Juniper:
Matrix-free continuation of limit cycles for bifurcation analysis of large thermoacoustic systems. J. Comput. Phys. 240: 225-247 (2013) - 2012
- [j1]Gary J. Chandler, Matthew P. Juniper, Joseph W. Nichols, Peter J. Schmid:
Adjoint algorithms for the Navier-Stokes equations in the low Mach number limit. J. Comput. Phys. 231(4): 1900-1916 (2012)
Conference and Workshop Papers
- 2022
- [c3]Yubiao Sun, Ushnish Sengupta, Matthew P. Juniper:
Physics-informed Deep Learning for Flow Modelling and Aerodynamic Optimization. SSCI 2022: 1149-1155 - 2021
- [c2]Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper:
Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models. ICCS (5) 2021: 408-419 - 2020
- [c1]Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young:
Ensembling geophysical models with Bayesian Neural Networks. NeurIPS 2020
Informal and Other Publications
- 2024
- [i9]Alexandros Kontogiannis, Scott V. Elgersma, Andrew J. Sederman, Matthew P. Juniper:
Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning. CoRR abs/2406.18464 (2024) - 2022
- [i8]Alexandros Kontogiannis, Matthew P. Juniper:
Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem. CoRR abs/2207.01466 (2022) - 2021
- [i7]Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper:
Online parameter inference for the simulation of a Bunsen flame using heteroscedastic Bayesian neural network ensembles. CoRR abs/2104.13201 (2021) - [i6]Ushnish Sengupta, Günther Waxenegger-Wilfing, Jan Martin, Justin Hardi, Matthew P. Juniper:
Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning. CoRR abs/2107.06396 (2021) - [i5]Ushnish Sengupta, Alexandros Kontogiannis, Matthew P. Juniper:
Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks. CoRR abs/2107.07863 (2021) - 2020
- [i4]Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young:
Ensembling geophysical models with Bayesian Neural Networks. CoRR abs/2010.03561 (2020) - [i3]Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper:
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles. CoRR abs/2011.02838 (2020) - [i2]Günther Waxenegger-Wilfing, Ushnish Sengupta, Jan Martin, Wolfgang Armbruster, Justin Hardi, Matthew P. Juniper, Michael Oschwald:
Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning. CoRR abs/2011.14985 (2020) - 2019
- [i1]Hans Yu, Matthew P. Juniper, Luca Magri:
Combined State and Parameter Estimation in Level-Set Methods. CoRR abs/1903.00321 (2019)
Coauthor Index
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