Courses

1. Neural Computing in Engineering (NUCL 575)

This course entails mathematical fundamentals of computing with neural networks. Survey of engineering applications. Computational metaphors from biological neurons. Artificial neural networks modeling of complex, nonlinear and ill-posed problems. Emphasizes engineering utilization of neural computing to diagnostics, control, safety, and decision-making problems. Typically offered in Fall.

2. Fuzzy Approaches in Engineering (NUCL 570)

This course entails the presentation of the mathematical fundamentals of fuzzy logic theory and a survey of engineering applications. Fuzzy sets; the extension principle; fuzzy numbers; fuzzy relations and composition; linguistic descriptions; implication operators and fuzzy algorithms are formally developed. Applications emphasize the engineering utilization of approximate reasoning to diagnostics, control, safety, and decision-making problems. Typically offered in Spring.

3. Big Data & Machine Learning (NUCL 597)

A graduate level course in Big Data & Machine Learning theory with engineering applications. This course aims to familiarize students with key information technologies and their underlying methods and techniques that are used to store, manipulate, analyze and exploit large volumes of data with emphasis in engineering applications and particularly nuclear data. Typically offered in Fall.

4. Nuclear Engineering Principles (NUCL 501)

A first course for graduate students desiring a nuclear engineering sequence and an elective for students in science and engineering. Nuclear structure and radiation interactions; radio isotope applications; basics of neutron and reactor physics; neutron diffusion and reactor criticality; reactor systems; heat generation; heat transfer and safety; nuclear materials. Typically offered in Fall.