| Uncertainty quantification for airfoil icing using polynomial chaos expansions AM DeGennaro, CW Rowley, L Martinelli Journal of Aircraft 52 (5), 1404-1411, 2015 | 44 | 2015 |
| Scalable Extended Dynamic Mode Decomposition using Random Kernel Approximation AM DeGennaro, NM Urban SIAM Journal on Scientific Computing 41 (3), A1482-A1499, 2019 | 26 | 2019 |
| Noise reduction in x-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models T Konstantinova, L Wiegart, M Rakitin, AM DeGennaro, AM Barbour Scientific Reports 11 (1), 14756, 2021 | 16 | 2021 |
| Co-design center for exascale machine learning technologies (exalearn) FJ Alexander, J Ang, JA Bilbrey, J Balewski, T Casey, R Chard, J Choi, ... The International Journal of High Performance Computing Applications 35 (6 …, 2021 | 12 | 2021 |
| Randomized algorithms for scientific computing (RASC) A Buluc, TG Kolda, SM Wild, M Anitescu, A Degennaro, J Jakeman, ... arXiv preprint arXiv:2104.11079, 2021 | 12 | 2021 |
| Model Structural Inference using Local Dynamic Operators A DeGennaro, N Urban, B Nadiga, T Haut International Journal for Uncertainty Quantification 9 (1), 59-83, 2018 | 10 | 2018 |
| Data-driven low-dimensional modeling and uncertainty quantification for airfoil icing A DeGennaro, CW Rowley, L Martinelli 33rd AIAA Applied Aerodynamics Conference, 3383, 2015 | 8 | 2015 |
| Uncertainty quantification for airfoil icing AM DeGennaro Princeton University, 2016 | 5 | 2016 |
| Machine Learning for analysis of speckle dynamics: quantification and outlier detection T Konstantinova, L Wiegart, M Rakitin, AM DeGennaro, AM Barbour Physical Review Research 4 (3), 033228, 2022 | 4 | 2022 |
| Machine learning enhances algorithms for quantifying non-equilibrium dynamics in correlation spectroscopy experiments to reach frame-rate-limited time resolution T Konstantinova, L Wiegart, M Rakitin, AM DeGennaro, AM Barbour arXiv preprint arXiv:2201.07889, 2022 | 2 | 2022 |
| Uncertainty quantification for cargo hold fires A DeGennaro, MW Lohry, L Martinelli, CW Rowley 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials …, 2016 | 2 | 2016 |
| Uncertainty quantification of the dynamic mode decomposition A DeGennaro, S Dawson, C Rowley APS Division of Fluid Dynamics Meeting Abstracts, R5. 010, 2015 | 2 | 2015 |
| Three-dimensional panel method hydrodynamic models of oscillating fins A DeGennaro 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and …, 2012 | 2 | 2012 |
| Machine Learning for Automating Analysis of Speckle Dynamics T Konstantinova, A DeGennaro, M Rakitin, A Barbour, L Wiegart Brookhaven National Laboratory (BNL), Upton, NY (United States), 2022 | | 2022 |
| Towards automating analysis of nonequilibrium X-ray Photon Correlation Spectroscopy with acquisition rate-limited time resolution T Konstantinova, L Wiegart, M Rakitin, A Degennaro, A Barbour APS March Meeting Abstracts 2022, W13. 005, 2022 | | 2022 |
| CNN-Encoder-Decoder Model T Konstantinova, A DeGennaro, M Rakitin, A Barbour, L Wiegart Brookhaven National Laboratory (BNL), Upton, NY (United States), 2021 | | 2021 |
| Black-Box Neural System Identification and Differentiable Programming to Improve Earth System Model Predictions February N Urban, A DeGennaro, Y Liu Artificial Intelligence for Earth System Predictability (AI4ESP …, 2021 | | 2021 |
| Building an AI-enhanced modeling framework to address multiscale predictability challenges Y Liu, N Urban, S Yoo, M Lin, T Zhang, X Zhou, Y Shan, C Xu, S Endo, ... Artificial Intelligence for Earth System Predictability (AI4ESP …, 2021 | | 2021 |
| Using Machine Learning for noise reduction in X-ray Photon Correlation Spectroscopy data to quantify time series dynamics T Konstantinova, L Wiegart, A Degennaro, A Barbour APS March Meeting Abstracts 2021, V60. 003, 2021 | | 2021 |
| Resource-Constrained Optimal Experimental Design AM DeGennaro, FJ Alexander arXiv preprint arXiv:2012.04067, 2020 | | 2020 |
