Theses

• Transport maps for accelerated Bayesian inference, Matthew Parno. MIT Computational Science and Engineering Ph.D. Thesis. October, 2014.

• A multiscale framework for Bayesian inference in elliptic problems, Matthew Parno. MIT Computation for Design and Optimization S.M. Thesis. May 2011.

Refereed Journals

Submitted

• A multiscale strategy for Bayesian inference using transport maps, M. Parno, T. Moselhy, and Y. Marzouk.  Submitted to SIAM Journal on Uncertainty Quantification, 2015.

• Transport map accelerated Markov chain Monte Carlo, M. Parno and Y. Marzouk, Submitted to the Journal of the American Statistical Society, 2014.  Download supplementary code.

Accepted

• Black box optimization using MODFLOW-FMP2: Determining trade-offs in crop distribution, K.R. Fowler, E.W. Jenkins, C. Ostrove, J.C. Chrispell, M.W. Farthing, M. Parno, Environmental Modelling & Software, Volume 69, July 2015, Pages 280-291.

• Applicability of Surrogates to Improve Efficiency of Particle Swarm Optimization, M.D. Parno, T.Hemker and K.R. Fowler, Engineering Optimization, Volume 44, Issue 5. 2012.

• Derivative-free optimization via evoluationary algorithms guiding local search (EAGLS) for MINLP, J.D. Griffin, K.R. Fowler, G.A. Gray, T. Hemker and M.D. Parno. Pacific Journal of Optimization, Volume 7, Number 3. 2011.

Technical Reports

• Bayesian data assimilation for stochastic multiscale models of transport in porous media., J. Ray, S. Lefantzi, K. Klise, L. Salazar, SA McKenna, B. van Bloemen Waanders, MD Parno, YM Marzouk. Sandia National Laboratories Technical Report, SAND Report SAND2011-6811. 2011.

• A Framework for Particle Swarm Optimization with Surrogate Functions, M.D. Parno, T.Hemker and K.R. Fowler. TU-Darmstadt Technical Report, TUD-CS-2009-0319. 2009.