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Courses & Educational Resources

CIVE 3337 Structural Analysis

Offerings: Spring 2023, Fall 2023.

Textbook: Structural Analysis by R. C. Hibbeler. Pearson, 2018, 10th ed. (Publisher site)

CIVE 6355 Structural Dynamics

Offering: Fall 2024.

Textbook: Structural Dynamics – Theory and Computation by Mario Paz and Young Hoon Kim. Springer, 2019 (6th Edition). (Publisher site)

CIVE 7397 Data-Driven Engineering

Offering: Spring 2024.

Topics:

  1. Basics of numerical methods.
  2. Data and models.
  3. Surrogate models.
  4. Reduced-order modeling.
  5. Fundamentals of Bayesian data analysis.
  6. Gaussian processes.
  7. Multi-fidelity Bayesian optimization.
  8. Optimization under uncertainty.

Description: Data-driven methods are revolutionizing the solution, prediction, and design of engineering systems. This course presents a collection of the fundamental techniques that enable data-driven engineering, including methods in machine learning, engineering mathematics, and scientific computing. An application focus of this course is in engineering design optimization, with approaches from multi-fidelity modeling and Bayesian optimization. This course caters to research-oriented graduate students in many engineering fields, such as structures, materials, and fluids, as well as thermal, chemical and electrical systems. The course offers a balanced view of data and models in the solution of engineering tasks. Students will have hands-on experience in implementing such methods via computer programming.

Educational Resources

The Lab maintains a wiki on several core subjects, including mathematics, computation, etc. This comes handy if you do research in uncertainty quantification.

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Augmented Reality (AR) models: