Dr. Doostan's research is focused on data-driven modeling, machine learning, and uncertainty quantification of complex systems. Of particular interest to his work is the development of data-driven and reduced order models for scalable solution of partial differential equations with random inputs. He is also involved in the development of efficient computational tools for large-scale statistical inverse problems, data-driven modeling, and big data compression research.
ASEN 3112 - Structures
Teaches Mechanics of Materials methods of stress and deformation analysis applicable to the design and verification of aircraft and space structures. It offers an introduction to matrix and finite element methods for truss structures, and to mechanical vibrations.
ASEN 5022 - Dynamics of Aerospace Structures
Spring 2019 / Spring 2021
Applies concepts covered in undergraduate dynamics, structures and mathematics to the dynamics of aerospace structural components, including methods of dynamic analysis, vibrational characteristics, vibration measurements and dynamic stability. Recommended prerequisite: ASEN 5012 or ASEN 5227 or MATH 2130 or APPM 3310 or equivalent or instructor consent required.
ASEN 6412 - Uncertainty Quantification
This advanced topics course provides an exploration of techniques for representation and propagation of uncertainty in PDE/ODE-based systems. Recommended prerequisites: APPM 5570 and ECEN 5612 (all minimum grade B) or equivalent courses with instructor consent.