| Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil SA Renganathan, R Maulik, V Rao Physics of Fluids 32 (4), 2020 | 73 | 2020 |
| Machine learning based algorithms for uncertainty quantification in numerical weather prediction models A Moosavi, V Rao, A Sandu Journal of Computational Science 50, 101295, 2021 | 43 | 2021 |
| A-posteriori error estimates for inverse problems V Rao, A Sandu arXiv preprint arXiv:1409.7129, 2014 | 33* | 2014 |
| A time-parallel approach to strong-constraint four-dimensional variational data assimilation V Rao, A Sandu Journal of Computational Physics 313, 583-593, 2016 | 32 | 2016 |
| A posteriori error estimates for the solution of variational inverse problems V Rao, A Sandu SIAM/ASA Journal on Uncertainty Quantification 3 (1), 737-761, 2015 | 26 | 2015 |
| A posteriori error estimates for the solution of variational inverse problems V Rao, A Sandu SIAM/ASA Journal on Uncertainty Quantification 3 (1), 737-761, 2015 | 26 | 2015 |
| A Hybrid Monte‐Carlo sampling smoother for four‐dimensional data assimilation A Attia, V Rao, A Sandu International Journal for Numerical Methods in Fluids 83 (1), 90-112, 2017 | 25 | 2017 |
| A posteriori error estimates for DDDAS inference problems V Rao, A Sandu Procedia Computer Science 29, 1256-1265, 2014 | 25 | 2014 |
| Robust Data Assimilation Using and Huber Norms V Rao, A Sandu, M Ng, ED Nino-Ruiz SIAM Journal on Scientific Computing 39 (3), B548-B570, 2017 | 21 | 2017 |
| Dynamic security constrained optimal power flow using finite difference sensitivities S Abhyankar, V Rao, M Anitescu 2014 IEEE PES General Meeting| Conference & Exposition, 1-5, 2014 | 20 | 2014 |
| A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: A case study over the midwestern United States J Wang, C Wang, V Rao, A Orr, E Yan, R Kotamarthi Geoscientific Model Development 12 (8), 3523-3539, 2019 | 19 | 2019 |
| Optimization under rare chance constraints S Tong, A Subramanyam, V Rao SIAM Journal on Optimization 32 (2), 930-958, 2022 | 18 | 2022 |
| Efficient high-dimensional variational data assimilation with machine-learned reduced-order models R Maulik, V Rao, J Wang, G Mengaldo, E Constantinescu, B Lusch, ... Geoscientific Model Development 15 (8), 3433-3445, 2022 | 17 | 2022 |
| A sampling approach for four dimensional data assimilation A Attia, V Rao, A Sandu International Conference on Dynamic Data-Driven Environmental Systems …, 2014 | 17 | 2014 |
| An adjoint-based scalable algorithm for time-parallel integration V Rao, A Sandu Journal of Computational Science 5 (2), 76-84, 2014 | 15 | 2014 |
| Scalable matrix-free adaptive product-convolution approximation for locally translation-invariant operators N Alger, V Rao, A Myers, T Bui-Thanh, O Ghattas SIAM Journal on Scientific Computing 41 (4), A2296-A2328, 2019 | 14 | 2019 |
| Order-Disorder Transitions in P Upreti, M Krogstad, C Haley, M Anitescu, V Rao, L Poudel, ... Physical Review Letters 128 (9), 095701, 2022 | 13 | 2022 |
| CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis SA Renganathan, V Rao, IM Navon Journal of Computational Physics 472, 111698, 2023 | 12 | 2023 |
| Dynamic sensor network configuration in infosymbiotic systems using model singular vectors A Sandu, A Cioaca, V Rao Procedia Computer Science 18, 1909-1918, 2013 | 10 | 2013 |
| Multifidelity Gaussian processes for failure boundary and probability estimation A Renganathan, V Rao, I Navon AIAA Scitech 2022 Forum, 0390, 2022 | 9 | 2022 |
Adrian SanduProfessor of Computer Science, Virginia TechVerified email at cs.vt.edu
