Ushnish Sengupta
Cited by
Cited by
Loop Motion in Triosephosphate Isomerase is not a Simple Open and Shut Case
Q Liao, Y Kulkarni, U Sengupta, D Petrovic, AJ Mulholland, ...
Journal of the American Chemical Society, 2018
Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly
U Sengupta, M Carballo-Pacheco, B Strodel
The Journal of chemical physics 150 (11), 115101, 2019
Markov models for the elucidation of allosteric regulation
U Sengupta, B Strodel
Philosophical Transactions of the Royal Society B 373 (Allostery and …, 2018
Bayesian machine learning for the prognosis of combustion instabilities from noise
U Sengupta, CE Rasmussen, MP Juniper
Journal of Engineering for Gas Turbines and Power 143 (7), 2021
Ensembling Geophysical Models with Bayesian Neural Networks
U Sengupta, M Amos, JS Hosking, CE Rasmussen, M Juniper, PJ Young
Advances in Neural Information Processing Systems (NeurIPS) 33, 2020
Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning
G Waxenegger-Wilfing, U Sengupta, J Martin, W Armbruster, J Hardi, ...
Chaos, 2020
Modelling an Air-Conditioner Fire in a Seminar Room using FDS
U Sengupta, AK Das
National Conference on Fire Research and Engineering, IIT Roorkee, 2014
Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models
ML Croci, U Sengupta, MP Juniper
International Conference on Computational Science, 408-419, 2021
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles
U Sengupta, M Croci, MP Juniper
3rd Workshop on Machine Learning and the Physical Sciences, NeurIPS 2020, 2020
Reducing Uncertainty in the Onset of Combustion Instabilities using Dynamic Pressure Information and Bayesian Neural Networks
M McCartney, U Sengupta, M Juniper
Journal of Engineering for Gas Turbines and Power, 2022
Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning
U Sengupta, G Waxenegger-Wilfing, J Martin, J Hardi, MP Juniper
Avoiding high-frequency thermoacoustic instabilities in liquid propellant rocket engines using Bayesian deep learning
U Sengupta, G Waxenegger-Wilfing, J Martin, J Hardi, M Juniper
APS Division of Fluid Dynamics Meeting Abstracts, R01. 026, 2020
Time-Accurate Calibration of a Thermoacoustic Model on Experimental Images of a Forced Premixed Flame
H Yu, U Sengupta, M Juniper, L Magri
72nd Annual Meeting of the APS Division of Fluid Dynamics, P06. 008, 2019
Physics-informed Deep Learning for simultaneous Surrogate Modelling and PDE-constrained Optimization
Y Sun, U Sengupta, M Juniper
Bulletin of the American Physical Society, 2022
Thermoacoustic stabilization of combustors with gradient-augmented Bayesian optimization and adjoint models
U Sengupta, M Juniper
International Journal of Spray and Combustion Dynamics, 2022
A continuous vertically resolved ozone dataset from the fusion of chemistry climate models with observations using a Bayesian neural network
M Amos, U Sengupta, P Young, JS Hosking
EarthArXiv, 2021
Bayesian Inference in Physics-Based Nonlinear Flame Models
ML Croci, U Sengupta, MP Juniper
NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021
Fusing model ensembles and observations together with Bayesian neural networks
M Amos, U Sengupta, S Hosking, P Young
EGU General Assembly Conference Abstracts, EGU21-11905, 2021
Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks
U Sengupta, A Kontogiannis, MP Juniper
Made for each other: adjoint solvers and high-dimensional gradient-augmented Bayesian optimization
U Sengupta, Y Sun, M Juniper
APS Division of Fluid Dynamics Meeting Abstracts, T24. 002, 2021
The system can't perform the operation now. Try again later.
Articles 1–20