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)

Matrix-free continuation of limit cycles for bifurcation analysis of large thermoacoustic systems. - Iain Waugh; Simon Illingworth; Matthew P. Juniper (J. Comput. Phys., 2013)

Stability analysis of thermo-acoustic nonlinear eigenproblems in annular combustors. Part I. Sensitivity. - Luca Magri; Michael Bauerheim; Matthew P. Juniper (J. Comput. Phys., 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)

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)

Combined state and parameter estimation in level-set methods. - Hans Yu; Matthew P. Juniper; Luca Magri (J. Comput. Phys., 2019)

Ensembling geophysical models with Bayesian Neural Networks. - Ushnish Sengupta; Matt Amos; J. Scott Hosking; Carl Edward Rasmussen; Matthew P. Juniper; Paul J. Young (NeurIPS, 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)

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)

Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models. - Maximilian L. Croci; Ushnish Sengupta; Matthew P. Juniper (ICCS (5), 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)

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)

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)

Physics-informed Deep Learning for Flow Modelling and Aerodynamic Optimization. - Yubiao Sun; Ushnish Sengupta; Matthew P. Juniper (SSCI, 2022)

Physics-Informed Compressed Sensing for PC-MRI: An Inverse Navier-Stokes Problem. - Alexandros Kontogiannis; Matthew P. Juniper (IEEE Trans. Image Process., 2023)

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)

Bayesian inference of mean velocity fields and turbulence models from flow MRI. - Alexandros Kontogiannis; Priya J. Nair; Michael Loecher; Daniel B. Ennis; Alison L. Marsden; Matthew P. Juniper (CoRR, 2024)