Dr. Doostan's research is focused on assimilation and propagation of uncertainties in complex PDE-based models for the purpose of model validation and verification. Of particular interest to his work is the development of 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 2001 - Aerospace 1: Introduction to Statics, Structures, and Materials
Fall 2018 / Fall 2019
Introduces models and analytical/numerical methods for statics and structural analysis. Topics include force/moment equilibrium, truss analysis, beam theory, stress/strain, failure criteria, and structural design. Matlab proficiency required. Offered fall only.
ASEN 5022 - Dynamics of Aerospace Structures
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.