| Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022 | 196 | 2022 |
| Transport map accelerated markov chain monte carlo MD Parno, YM Marzouk SIAM/ASA Journal on Uncertainty Quantification 6 (2), 645-682, 2018 | 177 | 2018 |
| Sampling via measure transport: An introduction Y Marzouk, T Moselhy, M Parno, A Spantini Handbook of uncertainty quantification 1, 2, 2016 | 155 | 2016 |
| An introduction to sampling via measure transport Y Marzouk, T Moselhy, M Parno, A Spantini arXiv preprint arXiv:1602.05023, 2016 | 84 | 2016 |
| The third Sandia Fracture Challenge: predictions of ductile fracture in additively manufactured metal SLB Kramer, A Jones, A Mostafa, B Ravaji, T Tancogne-Dejean, CC Roth, ... International Journal of Fracture 218, 5-61, 2019 | 77 | 2019 |
| Applicability of surrogates to improve efficiency of particle swarm optimization for simulation-based problems MD Parno, T Hemker, KR Fowler Engineering optimization 44 (5), 521-535, 2012 | 45 | 2012 |
| A multiscale strategy for Bayesian inference using transport maps M Parno, T Moselhy, Y Marzouk SIAM/ASA Journal on Uncertainty Quantification 4 (1), 1160-1190, 2016 | 37 | 2016 |
| A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-offs in crop selection KR Fowler, EW Jenkins, C Ostrove, JC Chrispell, MW Farthing, M Parno Environmental Modelling & Software 69, 280-291, 2015 | 32 | 2015 |
| Transport maps for accelerated Bayesian computation MD Parno Massachusetts Institute of Technology, 2015 | 22 | 2015 |
| A probabilistic optimal sensor design approach for structural health monitoring using risk-weighted f-divergence Y Yang, M Chadha, Z Hu, MA Vega, MD Parno, MD Todd Mechanical Systems and Signal Processing 161, 107920, 2021 | 18 | 2021 |
| High dimensional inference for the structural health monitoring of lock gates M Parno, D O'Connor, M Smith arXiv preprint arXiv:1812.05529, 2018 | 17 | 2018 |
| MUQ: The MIT uncertainty quantification library M Parno, A Davis, L Seelinger Journal of Open Source Software 6 (68), 3076, 2021 | 16 | 2021 |
| Derivative-free optimization via evolutionary algorithms guiding local search JD Griffin, KR Fowler, GA Gray, T Hemker, MD Parno Sandia National Laboratories, Albuquerque, NM, Tech. Rep. SAND2010-3023J, 2010 | 15 | 2010 |
| hIPPYlib-MUQ: A Bayesian inference software framework for integration of data with complex predictive models under uncertainty KT Kim, U Villa, M Parno, Y Marzouk, O Ghattas, N Petra ACM Transactions on Mathematical Software, 2023 | 13 | 2023 |
| Framework for particle swarm optimization with surrogate functions MD Parno, KR Fowler, T Hemker Darmstadt Technical University, Darmstadt, 2009 | 13 | 2009 |
| MIT uncertainty quantification (MUQ) library M Parno, A Davis, P Conrad, YM Marzouk | 11 | 2014 |
| COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications MA Rowland, TM Swannack, ML Mayo, M Parno, M Farthing, I Dettwiller, ... Scientific Reports 11 (1), 10875, 2021 | 9 | 2021 |
| ParticLS: Object-oriented software for discrete element methods and peridynamics AD Davis, BA West, NJ Frisch, DT O’Connor, MD Parno Computational Particle Mechanics, 1-13, 2021 | 8 | 2021 |
| Bonded discrete element simulations of sea ice with non‐local failure: Applications to Nares Strait B West, D O’Connor, M Parno, M Krackow, C Polashenski Journal of Advances in Modeling Earth Systems 14 (6), e2021MS002614, 2022 | 7 | 2022 |
| Accounting for model form uncertainty in Bayesian calibration of linear dynamic systems MK Ramancha, JP Conte, MD Parno Mechanical Systems and Signal Processing 171, 108871, 2022 | 7 | 2022 |
Youssef MarzoukProfessor, Massachusetts Institute of TechnologyVerified email at mit.edu
