Ushnish Sengupta

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Prof. Matthew P. JuniperProfessor of Thermofluids Dynamics, University of CambridgeVerified email at cam.ac.uk
Birgit StrodelProfessor, Computational Biochemistry, Research Centre Jülich & University Düsseldorf, GermanyVerified email at strodel.info
Maximilian CrociPostdoctoral researcher, Microsoft ResearchVerified email at cam.ac.uk
Matt AmosLancaster UniversityVerified email at lancaster.ac.uk
Günther Waxenegger-WilfingHead of System Analysis and Control Research Group at DLR Lampoldshausen, Professor at JMU WürzburgVerified email at dlr.de
Paul YoungLancaster University, UKVerified email at lancaster.ac.uk
J. Scott HoskingBritish Antarctic Survey, The Alan Turing InstituteVerified email at bas.ac.uk
Qinghua LiaoUniversity of BarcelonaVerified email at ub.edu
Kamerlin SC*Georgia Institute of TechnologyVerified email at gatech.edu
Yashraj KulkarniDepartment of Drug Design and Pharmacology, University of CopenhagenVerified email at sund.ku.dk
Marc van der KampSenior Lecturer, University of BristolVerified email at bris.ac.uk
Dusan PetrovicAstraZenecaVerified email at astrazeneca.com
Adrian MulhollandProfessor of Chemistry, University of BristolVerified email at bristol.ac.uk
Martin Carballo PachecoHoxton FarmsVerified email at hoxtonfarms.com
Justin S. HardiHead, Rocket Propulsion Technology Department, DLR Institute of Space PropulsionVerified email at dlr.de
Wolfgang ArmbrusterInstitute of Space Propulsion, German Aerospace Center (DLR)Verified email at dlr.de
Michael OschwaldGerman Aerospace CenterVerified email at dlr.de
Luca MagriImperial College LondonVerified email at imperial.ac.uk
Alexandros KontogiannisPh.D Student, University of CambridgeVerified email at cam.ac.uk
Joel Varun VasanthPhD Candidate, IIT Madras, ChennaiVerified email at smail.iitm.ac.in

Ushnish Sengupta

PhD Candidate in Engineering, University of Cambridge

Verified email at cam.ac.uk - Homepage

Thermoacoustic Instabilities Probabilistic Machine Learning Drug Design Molecular Dynamics Combustion


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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	59	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	40	2019
Markov models for the elucidation of allosteric regulation U Sengupta, B Strodel Philosophical Transactions of the Royal Society B 373 (Allostery and …, 2018	23	2018
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	15	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	14	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), 2021	13	2021
Data assimilation using heteroscedastic Bayesian neural network ensembles for reduced-order flame models ML Croci, U Sengupta, MP Juniper Computational Science–ICCS 2021: 21st International Conference, Krakow …, 2021	7*	2021
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	6	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	4	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	2	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	2	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	1	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	1	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	1	2019
Physics-informed Deep Learning for Flow Modelling and Aerodynamic Optimization Y Sun, U Sengupta, M Juniper 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 1149-1155, 2022		2022
Physics-informed Deep Learning for simultaneous Surrogate Modelling and PDE-constrained Optimization Y Sun, U Sengupta, M Juniper Journal of Computational Science, 2022		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		2022
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		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		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		2021

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