Data Science Research and Education in the University of Utah’s Department of Mechanical Engineering is developing and applying machine learning and artificial intelligence to generate new models and learn governing equations using simulated or physical data sets.

Faculty and Labs

Amir Arzani
Lab: Computational Biomechanics Group

Our group does computational mechanics research with various applications but with an emphasis on cardiovascular disease. Research Interests include: computational fluid dynamics (CFD), nonlinear solid mechanics (growth & remodeling), scientific machine learning (sparse data-driven modeling and physics-informed deep learning), mass transport, dynamical systems, flow physics and transport in chaotic flows with different applications (biological flows, environmental flows, and heat transfer)

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Jacob Hochhalter
Lab – Materials Prognosis from Integrated Modeling & Experiment (M’)

Researches emergent structural and material prognosis issues that involve the multiscale and stochastic nature of plasticity and fatigue cracking in structural materials. The research objective of the group is to leverage the ever-increasing capabilities in experimental observation and data analysis tools to provide new capabilities for prognosing reliability of advanced engineered structures and materials.

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Ashwin Renganathan
Lab – Computational complex engineered Systems Design Laboratory (CSDL)

Developing scalable computational methods for the design of next-generation complex engineered systems, such as aircraft. Specifically, we develop simulation-based design methods, where the goal is to make system-level design decisions in the presence of uncertainty and high-dimensional design spaces, with computationally expensive models of the system. Our research strives to make the design of complex engineered systems faster, cheaper, and more reliable. In this regard, we use and develop methods in approximation theory, optimization, and uncertainty quantification.

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Ashley Spear
Lab – Multiscale Mechanics & Materials

Conducts cutting-edge research at the nexus of materials and structures. We couple materials characterization with high-performance computing and data-driven analysis (including machine learning) to address a wide range of research topics that are especially pertinent to the defense, aerospace, and manufacturing communities.

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