Aerospace Engineering, Materials Science
Algorithms, Artificial Intelligence, Computational Tools for Data Science, Deep Learning, Graph Theory and Graph-based Methods, Machine Learning

Veera Sundararaghavan


Aerospace Engineering

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