| Using noisy or incomplete data to discover models of spatiotemporal dynamics PAK Reinbold, DR Gurevich, RO Grigoriev Physical Review E 101 (1), 010203, 2020 | 93 | 2020 |
| Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression PAK Reinbold, LM Kageorge, MF Schatz, RO Grigoriev Nature communications 12 (1), 3219, 2021 | 88 | 2021 |
| Robust and optimal sparse regression for nonlinear PDE models DR Gurevich, PAK Reinbold, RO Grigoriev Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (10), 2019 | 46 | 2019 |
| Data-driven discovery of partial differential equation models with latent variables PAK Reinbold, RO Grigoriev Physical Review E 100 (2), 022219, 2019 | 24 | 2019 |
| Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER) DR Gurevich, MR Golden, PAK Reinbold, RO Grigoriev arXiv preprint arXiv:2105.00048, 2021 | 6 | 2021 |
| Learning fluid dynamics using sparse physics-informed discovery of empirical relations (SPIDER) D Gurevich, P Reinbold, R Grigoriev APS Division of Fluid Dynamics Meeting Abstracts, F10. 002, 2021 | | 2021 |
| Learning fluid flow physics from noisy, incomplete, experimental data L Kageorge, P Reinbold, M Schatz, R Grigoriev APS Division of Fluid Dynamics Meeting Abstracts, K09. 018, 2020 | | 2020 |
| Recovering Quasi-2D Navier-Stokes Model Parameters via Weak Formulation P Reinbold, R Grigoriev Bulletin of the American Physical Society 63, 2018 | | 2018 |
Roman GrigorievGeorgia Institute of Technology在 physics.gatech.edu 的電子郵件地址已通過驗證
