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:
- Basics of numerical methods.
- Data and models.
- Surrogate models.
- Reduced-order modeling.
- Fundamentals of Bayesian data analysis.
- Gaussian processes.
- Multi-fidelity Bayesian optimization.
- 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.