Jason Hou

Associate Professor

North Carolina State University

United States

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About

Jason Hou is an Associate Professor at North Carolina State University, located in the United States. His research focuses on advanced reactor technologies, including machine learning applications in reactor safety analysis and optimization frameworks for advanced reactors. His recent work addresses topics such as predictive maintenance, uncertainty propagation, and control methodologies for nuclear reactor operations.

Articles (17)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2022

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2021

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2021

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2021

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2020

Collaborators (1)

Maria Avramova

Professor and University Faculty Scholar

North Carolina State University

UNITED STATES

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Jason Hou

Research Interests

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About

Articles (17)

Prototyping of a Machine Learning–Based Burnup Measurement Capability for Pebble Bed Reactor Fuel

Operation Optimization Framework for Advanced Reactors Using a Data-Driven Digital Twin

A System Predictive Maintenance Framework for Advanced Reactors Using a Data-Driven Digital Twin

Development and application of two-step uncertainty propagation and sensitivity analysis methodology for fast reactor safety analysis

Reinforcement Learning-Based Control Sequence Optimization for Advanced Reactors

A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation

Development and evaluation of parallel simulated annealing algorithm for reactor core optimization problems

A hybrid neutronics method with novel fission diffusion synthetic acceleration for criticality calculations

An Efficient High-to-Low Iterative Method for Light Water Reactor Analysis Based on NEAMS Tools

A Novel Method for Controlling Crud Deposition in Nuclear Reactors Using Optimization Algorithms and Deep Neural Network Based Surrogate Models

CTF-PARCS Core Multi-Physics Computational Framework for Efficient LWR Steady-State, Depletion and Transient Uncertainty Quantification

Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design

Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network

Summary of comparative analysis and conclusions from OECD/NEA LWR-UAM benchmark Phase I

Methodology for Discontinuity Factors Generation for Simplified P3 Solver Based on Nodal Expansion Formulation

Innovations in Multi-Physics Methods Development, Validation, and Uncertainty Quantification

Preliminary design and analysis of Liquid Fuel Molten Salt Reactor using multi-physics code GeN-Foam

Collaborators (1)

Maria Avramova

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