Ushnish Sengupta
Ushnish Sengupta
Senior AI Research Scientist, Mediatek Research
Verified email at - Homepage
Cited by
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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
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
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
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), 071001, 2021
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
Physics-informed Deep Learning for Simultaneous Surrogate Modelling and PDE-constrained Optimization of an airfoil geometry
Y Sun, U Sengupta, M Juniper
Computer Methods in Applied Mechanics and Engineering, 2023
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
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
International Journal of Spray and Combustion Dynamics, 2021
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
Bayesian inference in physics-based nonlinear flame models
ML Croci, U Sengupta, MP Juniper
NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021
Bayesian parameter inference of a vortically perturbed flame model for the prediction of thermoacoustic instability
ML Croci, JV Vasanth, U Sengupta, E Ekici, MP Juniper
NeurIPS 2022 AI for Science: Progress and Promises, 2022
Real-time parameter inference of nonlinear bluff-body-stabilized flame models using Bayesian neural network ensembles
ML Croci, U Sengupta, MP Juniper
SoTiC 2021 - Symposium on Thermoacoustics in Combustion: Industry meets Academia, 2021
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
Score Normalization for a Faster Diffusion Exponential Integrator Sampler
G Xia, D Danier, A Das, S Fotiadis, F Nabiei, U Sengupta, A Bernacchia
NeurIPS 2023 Workshop on Diffusion Models, 2023
Generative Diffusion Models for Radio Wireless Channel Modelling and Sampling
U Sengupta, C Jao, A Bernacchia, S Vakili, D Shiu
IEEE Global Communications Conference (GLOBECOM), 2023
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