Teaching

NE 405/505: Reactor Systems

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  • Brief course description:
    • This course provides a detailed discussion over nuclear power plant (NPP) systems, including Pressurized Water Reactor (PWR), Boiling Water Reactor (BWR), advanced light water reactors (LWR), as well as advanced non-LWRs. Topics to be covered include the PWR/BWR core design, primary loops, auxiliary and emergency systems, containment, reactor control and protection systems, accident and transient behaviors. 
    • The students are expected to learn how to apply knowledge in engineering sciences to design and understand complex systems, and gain an understanding of NPP engineering utilizing specific analytical skills acquired in other courses.
  • Frequency: Spring semesters, annually.
  • Prerequisites:
    • NE 400/500 – Nuclear Reactor Energy Conversion,
    • NE 401/501 – Reactor Analysis and Design, and 
    • NE 402/502 – Reactor Engineering

NE 491/591: Special Topics in Nuclear Engineering – Monte Carlo Methods for Radiation Transport

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  • Brief course description:
    • This course provides a detailed introduction over the fundamental concepts associated with the Monte Carlo (MC) method for particle/radiation transport. Students will be able to learn the fundamental and advanced topics on the application of MC to solve radiation transport problems in nuclear engineering.
    • Applications of generalized MC techniques using the MCNP code to solve neutron, photon, and electron radiation transport problems typically encountered in reactor physics, shielding, criticality safety, and radiation dosimetry will be addressed. The students will also learn how to use the MCNP code to solve these problems. 
  • Frequency: Fall semesters, 2021, 2022, and biennially starting from 2024.
  • Prerequisites:
    • NE 301 (Fundamentals of Nuclear Engineering), or basic understanding of nuclear reactor physics.
    • Background in Probability and Statistics, equivalent to ST 311 (Introduction to Statistics) and ST 371 (Introduction to Probability and Distribution Theory).
    • Programming experience (e.g., Python, MATLAB) is required.

NE 795: Advanced Topics in Nuclear Engineering I – Scientific Machine Learning

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  • Brief course description:
    • Scientific machine learning (SciML) is a burgeoning discipline in Artificial Intelligence and Artificial Intelligence (AI/ML) that blends scientific computing and ML. This course aims at augmenting the applications of AI/ML in scientific computing especially in the nuclear engineering area, and preparing the students for transformative solutions across various DOE missions, and for driving the next wave of data-driven scientific discovery in nuclear engineering.
    • After this course, the students will understand most of the fundamentals of supervised ML, including neural networks and other algorithms for regression/classification, as well as dimensionality reduction for unsupervised ML. The students will be able to implement ML algorithms in nuclear engineering scientific computing applications, with a heavy focus on uncertainty quantification, sensitivity analysis, calibration and inverse problems.
  • Frequency: Fall semesters, 2020, 2021, 2023, and biennially starting from 2024.
  • Prerequisites:
    • Background in calculus, linear algebra, scientific computing. Undergraduate level courses in these areas are sufficient.
    • Background in Probability and Statistics, equivalent to ST 311 (Introduction to Statistics) and ST 371 (Introduction to Probability and Distribution Theory).
    • Prior experience in Machine Learning is not required.
    • Programming experience is highly recommended. Python is the recommended programming language.
    • In the first lecture, instructions and resources to learn Python will be provided, as well as Python ecosystems and libraries that will be useful for this course.
    • Undergraduate students can take this class with instructor approval.

NE 795: Advanced Topics in Nuclear Engineering I – Advanced Scientific Machine Learning

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  • Brief course description:
    • This is an advanced level SciML course as a continuation of the NE 795 SciML course which focuses on fundamental algorithms. Topics to be covered including: Fundamentals of Information Theory, Clustering, Time Series Analysis and Forecasting, Convolutional Neural Networks (ConvNets), Recurrent Neural Networks (RNNs), Variational Inference Theory, Bayesian Neural Networks (BNNs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Normalizing Flows, Diffusion Models, etc.
  • Frequency: Fall semesters, biennially starting from 2023.
  • Prerequisites:
    • Prior experience in Machine Learning is required, equivalent to the NE 795 SciML course.
    • Or Instructor Approval