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Doostan, Alireza Associate Professor

Positions

Research Areas research areas

Research

research overview

  • 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.

keywords

  • Machine learning, Model reduction, Uncertainty Quantification, Computational Stochastic Mechanics, Computational Statistics

Publications

selected publications

Teaching

courses taught

  • ASEN 2001 - Aerospace 1: Introduction to Statics, Structures, and Materials
    Primary Instructor - 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.
  • ASEN 3112 - Structures
    Primary Instructor - Fall 2020
    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
    Primary Instructor - 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
    Primary Instructor - Spring 2020
    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.

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