| Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning R Dandekar, G Barbastathis medRxiv, 2020 | 174* | 2020 |
| Universal rim thickness in unsteady sheet fragmentation Y Wang, R Dandekar, N Bustos, S Poulain, L Bourouiba Physical review letters 120 (20), 204503, 2018 | 81 | 2018 |
| Bayesian neural ordinary differential equations R Dandekar, K Chung, V Dixit, M Tarek, A Garcia-Valadez, KV Vemula, ... arXiv preprint arXiv:2012.07244, 2020 | 55 | 2020 |
| A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread R Dandekar, C Rackauckas, G Barbastathis Patterns, 100145, 2020 | 53 | 2020 |
| Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China R Dandekar, G Barbastathis arXiv preprint arXiv:2003.09403, 2020 | 30 | 2020 |
| Neural Network aided quarantine control model estimation of COVID spread in Wuhan R Dandekar, G Barbastathis China. arXiv preprint arXiv 200309403, 2020 | 14 | 2020 |
| Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning. medRxiv R Dandekar, G Barbastathis Preprint posted online April 6, 2020 | 12 | 2020 |
| Film spreading from a miscible drop on a deep liquid layer R Dandekar, A Pant, BA Puthenveettil Journal of Fluid Mechanics 829, 304-327, 2017 | 12 | 2017 |
| Safe blues: A method for estimation and control in the fight against COVID-19 R Dandekar, SG Henderson, M Jansen, S Moka, Y Nazarathy, ... medRxiv, 2020.05. 04.20090258, 2020 | 10 | 2020 |
| Neural Network aided quarantine control model estimation of global Covid-19 spread. arXiv 2020 R Dandekar, G Barbastathis arXiv preprint arXiv:2004.02752, 0 | 7 | |
| Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics R Dandekar, SG Henderson, HM Jansen, J McDonald, S Moka, ... Patterns 2 (3), 2021 | 6 | 2021 |
| Neural Network aided quarantine control model estimation of global Covid-19 spread. Eprint R Dandekar, G Barbastathis arXiv preprint ArXiv:2004.02752, 2020 | 5 | 2020 |
| Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA R Dandekar, E Wang, G Barbastathis, C Rackauckas Health Data Science, 2021 | 3 | 2021 |
| Model-form epistemic uncertainty quantification for modeling with differential equations: Application to epidemiology E Acquesta, T Portone, R Dandekar, C Rackauckas, R Bandy, G Huerta Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | 1 | 2022 |
| Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning (preprint) R Dandekar, G Barbastathis | 1 | 2020 |
| Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning E Nieves, R Dandekar, C Rackauckas Frontiers in Systems Biology 4, 1338518, 2024 | | 2024 |
| Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations. E Acquesta, T Portone, R Bandy, R Dandekar, C Rackauckas Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Supplementary Information: Implications of delayed reopening in controlling the COVID-19 surge in Southern and West-Central USA R Dandekar, E Wang, G Barbastathis, C Rackauckas | | 2021 |
| Learning Missing Mechanisms in a Dynamical System from a Subset of State Variable Observations. T Portone, E Acquesta, R Dandekar, C Rackauckas Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| Research Article Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA R Dandekar, E Wang, G Barbastathis, C Rackauckas | | 2021 |
George BarbastathisProfessor of Mechanical Engineering, Massachusetts Institute of TechnologyVerified email at mit.edu
