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Xun Huan

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Prof. Huan’s research broadly revolves around uncertainty quantification, data-driven modeling, and numerical optimization. He focuses on methods to bridge together models and data: e.g., optimal experimental design, Bayesian statistical inference, uncertainty propagation in high-dimensional settings, and algorithms that are robust to model misspecification. He seeks to develop efficient numerical methods that integrate computationally-intensive models with big data, and combine uncertainty quantification with machine learning to enable robust and reliable prediction, design, and decision-making.

Optimal experimental design seeks to identify experiments that produce the most valuable data. For example, when designing a combustion experiment to learn chemical kinetic parameters, design condition A maximizes the expected information gain. When Bayesian inference is performed on data from this experiment, we indeed obtain “tighter” posteriors (with less uncertainty) compared to those obtained from suboptimal design conditions B and C.

Venkat Raman

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Prof. Raman’s work focuses on the simulation of large scale combustion systems – aircraft engines, stationary power turbines, hypersonic engines – with the goal of advancing computations-aided systems design. This involves large scale computations accounting for detailed behavior of the chaotic turbulent flow in these systems, combined with enabling science in computational chemistry and algorithms. One aspect of my research is the prediction of rare events that lead to catastrophic system failure (as in flight crash, engine failure etc.). This work also involves the understanding of uncertainty in models, and streamlining knowledge in the form of mathematical models.

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Karthik Duraisamy

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Karthik Duraisamy, PhD, is Associate Professor of Aerospace Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Duraisamy’s group focuses on data-driven modeling of computational physics problems. Specifically, we use statistical inversion and physics-informed machine learning techniques to augment existing computational models. Another focus area is formal reduced order modeling using data-driven decompositions.

Our application areas are in turbulence, combustion and materials physics.

Turbulent flow in a trailing vortex.

Turbulent flow in a trailing vortex.