| Multifidelity aerodynamic flow field prediction using random forest-based machine learning J Nagawkar, L Leifsson Aerospace Science and Technology 123, 107449, 2022 | 19 | 2022 |
| Single-and multipoint aerodynamic shape optimization using multifidelity models and manifold mapping J Nagawkar, J Ren, X Du, L Leifsson, S Koziel Journal of Aircraft 58 (3), 591-608, 2021 | 17 | 2021 |
| The lemcotec 1½ stage film-cooled HP turbine: design, integration and testing in the Oxford turbine research facility PF Beard, MG Adams, JR Nagawakar, MR Stokes, F Wallin, DN Cardwell, ... 13 th European Conference on Turbomachinery Fluid dynamics & Thermodynamics, 2019 | 11 | 2019 |
| Applications of polynomial chaos-based cokriging to aerodynamic design optimization benchmark problems J Nagawkar, LT Leifsson, X Du AIAA Scitech 2020 Forum, 0542, 2020 | 10 | 2020 |
| Applications of polynomial chaos-based cokriging to simulation-based analysis and design under uncertainty J Nagawkar, L Leifsson International Design Engineering Technical Conferences and Computers and …, 2020 | 8 | 2020 |
| Aerodynamic shape optimization using gradient-enhanced multifidelity neural networks JR Nagawkar, LT Leifsson, P He AIAA SciTech 2022 Forum, 2350, 2022 | 6 | 2022 |
| Efficient Global Sensitivity Analysis of Model-Based Ultrasonic Nondestructive Testing Systems Using Machine Learning and Sobol’Indices J Nagawkar, L Leifsson Journal of Nondestructive Evaluation, Diagnostics and Prognostics of …, 2021 | 4 | 2021 |
| Multifidelity Aerodynamic Flow Field Prediction Using Conditional Adversarial Networks MW Brittain, JR Nagawkar, P Wei, LT Leifsson AIAA Aviation 2021 Forum, 3047, 2021 | 4 | 2021 |
| Unsteady 3D CFD analysis of a film-cooled 11⁄ 2 stage turbine J Nagawkar | 4 | 2016 |
| Iterative global sensitivity analysis algorithm with neural network surrogate modeling YC Liu, J Nagawkar, L Leifsson, S Koziel, A Pietrenko-Dabrowska International Conference on Computational Science, 298-311, 2021 | 2 | 2021 |
| Gradient-enhanced multifidelity neural networks for high-dimensional function approximation J Nagawkar, L Leifsson International Design Engineering Technical Conferences and Computers and …, 2021 | 1 | 2021 |
| Multifidelity Aerodynamic Flow Field Prediction Using Random Forests JR Nagawkar, MW Brittain, LT Leifsson AIAA Aviation 2021 Forum, 3089, 2021 | 1 | 2021 |
| Development of an Open-source Flutter Prediction Framework for the Common Research Model Wing BT Crow, JR Nagawkar, LT Leifsson, AS Thelen AIAA Scitech 2021 Forum, 1590, 2021 | 1 | 2021 |
| Model-Based Sensitivity Analysis of Nondestructive Testing Systems Using Machine Learning Algorithms J Nagawkar, L Leifsson, R Miorelli, P Calmon Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020 | 1 | 2020 |
| Global Surrogate Modeling by Neural Network-Based Model Uncertainty L Leifsson, J Nagawkar, L Barnet, K Bryden, S Koziel, ... International Conference on Computational Science, 425-434, 2022 | | 2022 |
| Multifidelity machine learning methods for flow field prediction and aerodynamic shape optimization JR Nagawkar Iowa State University, 2022 | | 2022 |
| Sensitivity Analysis and Optimal Design with PC-co-kriging L Leifsson, J Nagawkar Surrogate Modeling For High-frequency Design: Recent Advances, 405-425, 2022 | | 2022 |
| Meta-MAPOD X Du, J Nagawkar, L Leifsson, W Meeker, P Gurrala, J Song, R Roberts Review of Progress in Quantitative Nondestructive Evaluation, 2019 | | 2019 |
| Model-assisted reliability analysis of nondestructive testing systems using polynomial chaos-based Cokriging X Du, L Leifsson, J Nagawkar Review of Progress in Quantitative Nondestructive Evaluation, 2019 | | 2019 |
Leifur LeifssonAssociate Professor, School of Aeronautics and Astronautics, Purdue UniversityVerified email at purdue.edu
