734-647-6391

Applications:
Aerospace Engineering, Biological Sciences, Complex Systems, Education, Epidemiology, Precision Medicine, Transportation Research
Methodologies:
Algorithms, Artificial Intelligence, Classification, Computational Tools for Data Science, Data Visualization, Deep Learning, Human-Computer Interaction, Machine Learning, Mathematics, Predictive Modeling, Real-time Data Processing, Signal Processing, Spatio-Temporal Data Analysis, Statistical Modeling
Relevant Projects:

NSF, DOE, DOD, NIH, Industry


Bogdan I. Epureanu

Professor

Department of Mechanical Engineering


Affiliation(s):

Electrical Engineering and Computer Science

• Computational dynamics focused on nonlinear dynamics and finite elements (e.g., a new approach for forecasting bifurcations/tipping points in aeroelastic and ecological systems, new finite element methods for thin walled beams that leads to novel reduced order models).
• Modeling nonlinear phenomena and mechano-chemical processes in molecular motor dynamics, such as motor proteins, toward early detection of neurodegenerative diseases.
• Computational methods for robotics, manufacturing, modeling multi-body dynamics, developed methods for identifying limit cycle oscillations in large-dimensional (fluid) systems.
• Turbomachinery and aeroelasticity providing a better understanding of fundamental complex fluid dynamics and cutting-edge models for predicting, identifying and characterizing the response of blisks and flade systems through integrated experimental & computational approaches.
• Structural health monitoring & sensing providing increased sensibility / capabilities by the discovery, characterization and exploitation of sensitivity vector fields, smart system interrogation through nonlinear feedback excitation, nonlinear minimal rank perturbation and system augmentation, pattern recognition for attractors, damage detection using bifurcation morphing.