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E M X97 E M 397, 697, 997  Topics in Advanced Engineering Mechanics  3,6,9 Hours

3,6,9 Lecture Hours  0 Lab Hours  
For each semester hour of credit earned, one lecture hour a week for one semester.
Pre/Corequisites: Graduate standing and consent of instructor.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Advanced Topics in Viscoelasticity.

Topic 2: Individual Research.

Topic 3: Advanced Computational Flows and Transport.

Topic 4: Grid Generation and Adaptive Grids.

Topic 5: Adaptive Boundary/Finite Element Methods.

Topic 6: Introduction to Machine Learning and Data Sciences. Discuss machine learning algorithms, including linear models, kernel machines, tree-based algorithms, density estimation, clustering, dimensionality reduction and deep neural networks; foundations of machine learning, including optimization theory, probability theory, advanced linear algebra, statistics; evaluation of machine learning methods; and convolutational neural networks. Engineering Mechanics 397 (Topic: Intro to Machine Learning) and 397 (Topic 6) may not both be counted.
  

M E X97 M E 397  Topics in Current Engineering Studies  3 Hours

3 Lecture Hours  0 Lab Hours  
The equivalent of three class hours a week for one semester.
Pre/Corequisites: Graduate standing and consent of the graduate adviser.  
Grading: Student Option  
Repeatable for credit: May be repeated for credit when the topics vary.  
Academic Level: Doctoral  
Topic 1: Projects.

Topic 2: Nuclear Engineering Materials.

Topic 3: Facilitating Process Improvement.

Topic 8: Energy and the Environment. Additional prerequisite: Consent of instructor.

Topic 9: Nuclear Fuel Cycles.

Topic 11: Decision and Risk Analysis. Fundamentals of decision analysis and risk assessment; mathematical and psychological aspects of decision making, especially under uncertain conditions; engineering/project management applications.

Topic 13: Large-Scale Systems Optimization.

Topic 16: Nuclear Reactor Safety.

Topic 18: Gas Radiation.

Topic 26: Nuclear Health Physics.

Topic 29: Thermomechanical Processing of Polymers.

Topic 33: Physical Gas Dynamics.

Topic 34: Computational Geometry of Engineering Design.

Topic 35: Statistical Design of Experiments.

Topic 36: Advanced Stochastic Processes.

Topic 37: Nuclear Engineering Materials.

Topic 38: Sampling.

Topic 39: Systems Simulation.

Topic 40: Advanced Topics in Computer-Aided Design.

Topic 42: Combinatorial Optimization.
  

E M X97.1 E M 397.1, 697.1, 997.1  Advanced Topics in Viscoelasticity  3,6,9 Hours

3,6,9 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

E M X97.2 E M 397.2, 697.2, 997.2  Individual Research  3,6,9 Hours

3,6,9 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

E M X97.3 E M 397.3, 697.3, 997.3  Advanced Computational Flows and Transport  3,6,9 Hours

3,6,9 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

E M X97.4 E M 397.4, 697.4, 997.4  Grid Generation and Adaptive Grids  3,6,9 Hours

3,6,9 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

E M X97.5 E M 397.5, 697.5, 997.5  Adaptive Boundary/Finite Element Methods  3,6,9 Hours

3,6,9 Lecture Hours  0 Lab Hours  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

M E X97.8 M E 397.8  Energy and the Environment  3 Hours

3 Lecture Hours  0 Lab Hours  
Pre/Corequisites: Consent of instructor.  
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral  

E M X97.6 E M 397.6, 697.6, 997.6  Introduction to Machine Learning and Data Sciences  3,6,9 Hours

3,6,9 Lecture Hours  0 Lab Hours  
Discuss machine learning algorithms, including linear models, kernel machines, tree-based algorithms, density estimation, clustering, dimensionality reduction and deep neural networks; foundations of machine learning, including optimization theory, probability theory, advanced linear algebra, statistics; evaluation of machine learning methods; and convolutational neural networks. Engineering Mechanics 397 (Topic: Intro to Machine Learning) and 397 (Topic 6) may not both be counted.
Grading: Student Option  
Repeatable for credit: No  
Academic Level: Doctoral