| Effectively subsampled quadratures for least squares polynomial approximations P Seshadri, A Narayan, S Mahadevan SIAM/ASA Journal on Uncertainty Quantification 5 (1), 1003-1023, 2017 | 69* | 2017 |
| Leakage Uncertainties in Compressors: The Case of Rotor 37 P Seshadri, GT Parks, S Shahpar AIAA Journal of Propulsion and Power 31 (1), 2015 | 63 | 2015 |
| Turbomachinery active subspace performance maps P Seshadri, S Shahpar, P Constantine, G Parks, M Adams Journal of Turbomachinery 140 (4), 041003, 2018 | 60 | 2018 |
| AeroVR: An immersive visualisation system for aerospace design and digital twinning in virtual reality SK Tadeja, P Seshadri, PO Kristensson The Aeronautical Journal 124 (1280), 1615-1635, 2020 | 56 | 2020 |
| Robust compressor blades for desensitizing operational tip clearance variations P Seshadri, S Shahpar, GT Parks Turbo Expo: Power for Land, Sea, and Air 45608, V02AT37A043, 2014 | 56 | 2014 |
| Numerical evaluation of entropy generation in isolated airfoils and Wells turbines T Ghisu, F Cambuli, P Puddu, N Mandas, P Seshadri, GT Parks Meccanica 53, 3437-3456, 2018 | 50 | 2018 |
| A density-matching approach for optimization under uncertainty P Seshadri, P Constantine, G Iaccarino, G Parks Computer Methods in Applied Mechanics and Engineering, 2016 | 41* | 2016 |
| Dimension reduction via gaussian ridge functions P Seshadri, S Yuchi, GT Parks SIAM/ASA Journal on Uncertainty Quantification 7 (4), 1301-1322, 2019 | 35 | 2019 |
| Understanding micro air vehicle flapping-wing aerodynamics using force and flowfield measurements P Seshadri, M Benedict, I Chopra Journal of Aircraft 50 (4), 1070-1087, 2013 | 31 | 2013 |
| Effective-Quadratures (EQ): Polynomials for Computational Engineering Studies P Seshadri, G Parks The Journal of Open Source Software 2 (11), 2017 | 30 | 2017 |
| Sensitivity Analysis of a Coupled Hydrodynamic-Vegetation Model Using the Effectively Subsampled Quadratures Method TS Kalra, A Aretxabaleta, P Seshadri, NK Ganju, A Beudin Geosci. Model Dev., 2017 | 25 | 2017 |
| Discovering a one-dimensional active subspace to quantify multidisciplinary uncertainty in satellite system design X Hu, GT Parks, X Chen, P Seshadri Advances in space research 57 (5), 1268-1279, 2016 | 25 | 2016 |
| Digital twin assessments in virtual reality: An explorational study with aeroengines SK Tadeja, Y Lu, P Seshadri, PO Kristensson 2020 IEEE aerospace conference, 1-13, 2020 | 21 | 2020 |
| Uncertainty quantification for data-driven turbulence modelling with Mondrian forests A Scillitoe, P Seshadri, M Girolami Journal of Computational Physics 430, 110116, 2021 | 18 | 2021 |
| Automatic borescope damage assessments for gas turbine blades via deep learning CY Wong, P Seshadri, GT Parks AIAA Scitech 2021 Forum, 1488, 2021 | 18 | 2021 |
| Bayesian Assessments of Aeroengine Performance with Transfer Learning P Seshadri, AB Duncan, G Thorne, G Parks, RV Dıaz, M Girolami Data-Centric Engineering 3 (E29), 2022 | 15 | 2022 |
| Spatial flow-field approximation using few thermodynamic measurements—Part I: Formulation and area averaging P Seshadri, D Simpson, G Thorne, A Duncan, G Parks Journal of Turbomachinery 142 (2), 021006, 2020 | 15 | 2020 |
| Gradient-enhanced least-square polynomial chaos expansions for uncertainty quantification and robust optimization T Ghisu, DI Lopez, P Seshadri, S Shahpar AIAA AVIATION 2021 FORUM, 3073, 2021 | 14 | 2021 |
| Discussion on “Performance analysis of Wells turbine blades using the entropy generation minimization method” by Shehata, AS, Saqr, KM, Xiao, Q., Shahadeh, MF and Day, A. T Ghisu, P Puddu, F Cambuli, N Mandas, P Seshadri, GT Parks Renewable Energy 118, 386-392, 2018 | 14 | 2018 |
| Extremum sensitivity analysis with polynomial Monte Carlo filtering CY Wong, P Seshadri, G Parks Reliability Engineering & System Safety 212, 107609, 2021 | 13* | 2021 |