Alex Gorodetsky

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Alex Gorodetsky’s research is at the intersection of applied mathematics, data science, and computational science, and is focused on enabling autonomous decision making under uncertainty. He is especially interested in controlling, designing, and analyzing autonomous systems that must act in complex environments where observational data and expensive computational simulations must work together to ensure objectives are achieved. Toward this goal, he pursues research in wide-ranging areas including uncertainty quantification, statistical inference, machine learning, control, and numerical analysis. His methodology is to increase scalability of probabilistic modeling and analysis techniques such as Bayesian inference and uncertainty quantification. His current strategies to achieving scalability revolve around leveraging computational optimal transport, developing tensor network learning algorithms, and creating new multi-fidelity information fusion approaches.

Sample workflow for enabling autonomous decision making under uncertainty for a drone operating in a complex environment. We develop algorithms to compress simulation data by exploiting problem structure. We then embed the compressed representations onto onboard computational resources. Finally, we develop approaches to enable the drone to adapt, learn, and refine knowledge by interacting with, and collecting data from, the environment.

Veera Sundararaghavan

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Veera Sundararaghavan is a Professor of Aerospace Engineering at the University of Michigan – Ann Arbor and the director of Multiscale Structural Simulations Laboratory. His research is on multi-length scale computational techniques for modelling and design of aerospace materials with a focus on microstructural mechanics (crystal plasticity, homogenization) and molecular simulation. He is particularly interested in new computational techniques that can revolutionize the way we compute in materials science: machine learning and quantum computing algorithms. He has made important contributions in the area of integrated computational materials engineering (ICME) including reduced order representations for microstructure-process-property relationships, Markov random fields approach for microstructure reconstruction, and parallel, multiscale algorithms for optimizing deformation, fatigue, failure and oxidation response in polycrystalline alloys, high temperature ceramic matrix composites and energetic composites. Methods of choice for data science include deep Boltzmann machines, undirected graph models (Markov random fields) and Support vector machines.

An illustration of the hybrid Quantum-Classical computation technique: Quantum Annealer is used as a Boltzmann sampler while the gradient optimization is carried out using classical computation

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.