Welcome to the Uncertainty Quantification Group, in the Department of Aeronautics and Astronautics at MIT. We are part of the Aerospace Computational Design Laboratory and affiliated with the Center for Computational Engineering.

Research Overview

Our research focuses on advancing fundamental computational methodology for uncertainty quantification and statistical inference in complex physical systems, and using these tools to address challenges in modeling energy conversion and environmental applications.

We tackle a broad range of projects, but most involve aspects of a few core questions:

  • How to quantify confidence in computational predictions?
  • How to build or refine models of complex physical processes from indirect and limited observations?
  • What information is needed to drive inference, design, and control?

Featured Publications

A. Spantini, R. Baptista and Y. M. Marzouk, Coupling techniques for nonlinear ensemble filtering,Preprint, (2019).
O. Zahm, T. Cui, K. Law, A. Spantini and Y. M. Marzouk, Certified dimension reduction in nonlinear Bayesian inverse problems,Preprint, (2018).
M. Parno and Y. M. Marzouk, Transport map accelerated Markov chain Monte Carlo,SIAM/ASA Journal on Uncertainty Quantification, 6 (2018), pp. 645–682.
A. Spantini, D. Bigoni and Y. M. Marzouk, Inference via low-dimensional couplings,The Journal of Machine Learning Research, 19 (2018), pp. 1–71.
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