| Distinguishing and integrating aleatoric and epistemic variation in uncertainty quantification K Chowdhary, P Dupuis ESAIM: Mathematical Modelling and Numerical Analysis 47 (3), 635-662, 2013 | 65 | 2013 |
| The Uncertainty Quantification Toolkit (UQTk). B Debusschere, K Sargsyan, C Safta, KS Chowdhary Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2015 | 64 | 2015 |
| Robust bounds on risk-sensitive functionals via Rényi divergence R Atar, K Chowdhary, P Dupuis SIAM/ASA Journal on Uncertainty Quantification 3 (1), 18-33, 2015 | 63 | 2015 |
| Probabilistic methods for sensitivity analysis and calibration in the NASA challenge problem C Safta, K Sargsyan, HN Najm, K Chowdhary, B Debusschere, LP Swiler, ... Journal of Aerospace Information Systems 12 (1), 219-234, 2015 | 28* | 2015 |
| Inference of reaction rate parameters based on summary statistics from experiments M Khalil, K Chowdhary, C Safta, K Sargsyan, HN Najm Proceedings of the Combustion Institute 36 (1), 699-708, 2017 | 17 | 2017 |
| Projection-based model reduction of dynamical systems using space–time subspace and machine learning C Hoang, K Chowdhary, K Lee, J Ray Computer Methods in Applied Mechanics and Engineering 389, 114341, 2022 | 16 | 2022 |
| Machine learning models of errors in large eddy simulation predictions of surface pressure fluctuations MF Barone, J Ling, K Chowdhary, W Davis, J Fike 47th AIAA Fluid Dynamics Conference, 3979, 2017 | 15 | 2017 |
| Bayesian estimation of Karhunen–Loève expansions; A random subspace approach K Chowdhary, HN Najm Journal of Computational Physics 319, 280-293, 2016 | 14 | 2016 |
| Data free inference with processed data products K Chowdhary, HN Najm Statistics and Computing 26, 149-169, 2016 | 13 | 2016 |
| Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models K Chowdhary, C Hoang, K Lee, J Ray, VG Weirs, B Carnes Computer Methods in Applied Mechanics and Engineering 401, 115396, 2022 | 12 | 2022 |
| Development of machine learning models for turbulent wall pressure fluctuations J Ling, MF Barone, W Davis, K Chowdhary, J Fike 55th AIAA Aerospace Sciences Meeting, 0755, 2017 | 11 | 2017 |
| An improved hyperbolic embedding algorithm K Chowdhary, TG Kolda Journal of Complex Networks 6 (3), 321-341, 2018 | 9 | 2018 |
| Quadrature methods for the calculation of subgrid microphysics moments K Chowdhary, M Salloum, B Debusschere, VE Larson Monthly Weather Review 143 (7), 2955-2972, 2015 | 8 | 2015 |
| UQ toolkit B Debusschere, K Sargsyan, C Safta, K Chowdhary UQToolkit, 2015 | 8 | 2015 |
| Handbook of Uncertainty Quantification B Debusschere, K Sargsyan, C Safta, K Chowdhary, R Ghanem, ... Springer International Publishing, 2017 | 7 | 2017 |
| Inference given Summary Statistics. HN Najm, KS Chowdhary Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2015 | 7 | 2015 |
| Multimodal Bayesian registration of noisy functions using Hamiltonian Monte Carlo JD Tucker, L Shand, K Chowdhary Computational Statistics & Data Analysis 163, 107298, 2021 | 6 | 2021 |
| Biological and Environmental Research Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Biological and … A Arkin, DC Bader, R Coffey, K Antypas, D Bard, E Dart, S Dosanjh, ... US Department of Energy, Washington, DC (United States). Advanced Scientific …, 2016 | 4 | 2016 |
| Uncertainty enabled design of an acceleration switch MR Brake, JE Massad, RC Smith, B Beheshti, K Chowdhary, J Davis, ... ASME International Mechanical Engineering Congress and Exposition 54938, 607-616, 2011 | 4 | 2011 |
| UQTk: A Flexible Python/C++ Toolkit for Uncertainty Quantification. B Debusschere, K Sargsyan, C Safta, P Rai, KS Chowdhary Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2018 | 2 | 2018 |
