Xingang Zhao R&D Associate Staff Contact zhaox2@ornl.gov | 865.314.1618 All Publications Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems Digital Twin Development of FASTR Bayesian Network–Based Fault Diagnostic System for Nuclear Power Plant Assets... CTF: A modernized, production-level, thermal hydraulic solver for the solution of industry-relevant challenge problems in pre... The pursuit of net-positive sustainability for industrial decarbonization with hybrid energy systems... The Role of Hybrid Energy Systems in Decarbonizing Industry: A Carbon Handprint–Based Case Study... Constructing A New CHF Look-Up Table Based on the Domain Knowledge Informed Machine Learning Methodology... System risk quantification and decision making support using functional modeling and dynamic Bayesian network... Unified Domain Knowledge Informed Machine Learning Model for CHF Prediction... Artificial Reasoning System for Symptom-Based Conditional Failure Probability Estimation Using Bayesian Network... Physics-Informed Machine Learning-Aided System Space Discretization... Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven... Improved departure from nucleate boiling prediction in rod bundles using a physics-informed machine learning-aided framework CTF Validation and Verification Multiphysics coupling plan... Subchannel codes: CTF and VIPRE-01... Physics-informed machine learning-aided framework for CASL challenge problem solving—a demonstration and prospects... A ROBUST MECHANISTIC APPROACH TO PREDICTION OF DEPARTURE FROM NUCLEATE BOILING Prognostic model and failure mechanisms of steam generators in Sodium-Cooled fast reactors Machine Learning for Time-Series Prediction of the Cryogenic Moderator System in Oak Ridge National Laboratory's Spallation Neutron Source Facility CTF Validation and Verification: Version 4.4 CTF Validation and Verification: Version 4.3 Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework... Technical Challenges and Gaps in Integration of Advanced Sensors, Instrumentation, and Communication Technologies with Digita... Key Links Google Scholar ORCID LinkedIn ResearchGate Organizations Fusion and Fission Energy and Science Directorate Nuclear Energy and Fuel Cycle Division Advanced Reactor Engineering and Development Section Modern Nuclear I&C Group Artificial Intelligence (AI) Initiative User Facilities Spallation Neutron Source
