Home
The research in the ARTISANS group revolves around uncertainty quantification (UQ) and scientific machine learning (SciML). The goal is to combine SciML, experimentation and modeling & simulation (M&S) into a unified approach to improve the predictive capabilities of computer models and enable reduced reliance on expensive measurement data. Additionally, the application of such research will be focused on risk and economics evaluations of advanced nuclear reactors, such as small modular reactors and micro-reactors. The ultimate goal is to dramatically reduce the capital and operating costs of nuclear systems to maintain global technology leadership for nuclear energy.
Our main research interests include: (1) calibration, validation, data assimilation, uncertainty and sensitivity analysis; (2) computational statistics, reduced order modeling; (3) Bayesian inverse problems; (4) scientific machine learning and deep generative learning; (5) system thermal-hydraulics, nuclear fuel performance modeling, multi-physics coupled simulation; (6) advanced reactors, small modular reactors, micro-reactors.
Recent News
- Aidan’s journal paper accepted by Applied Thermal Engineering (Impact Factor 6.9)Aidan’s journal paper titled “Physics-Based Hybrid Machine Learning for Critical Heat Flux Prediction with Uncertainty Quantification” has been accepted for publication at Applied Thermal Engineering…
- Lauren’s paper accepted by Nuclear Science and EngineeringLauren’s journal paper titled “Bayesian Calibration and Sensitivity Analysis of Rayleigh Scattering Fiber Optic Distributed Temperature Sensing in Water Flow Loop” has been accepted for…
- Lauren won the FY 25 Innovations in Nuclear Energy Student Paper Competition AwardCongratulations to Lauren for winning the “FY 25 Innovations in Nuclear Energy Student Paper Competition Award”. Lauren has been recognized for her work on “Bayesian…
- Alie’s paper accepted by Nuclear TechnologyAlie’s journal paper titled “Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap” has been accepted…
- Aidan selected to be a NESD delegateCongratulations to Aidan for being selected for the Nuclear Engineering Student Delegation.
- Farah selected for ANS Graduate ScholarshipCongratulations to Farah for being selected as the recipient of the 2025-2026 American Nuclear Society (ANS) Graduate Scholarship for $3000.
- Lauren passed PhD Qualification ExamCongratulations to Lauren for successfully passing her PhD Qualification Exam on “Review of Foundation Models for Scientific Machine Learning Applications”.
- Alie passed PhD Qualification ExamCongratulations to Alie for successfully passing her PhD Qualification Exam on “Review of Physics-Informed Diffusion Models”.
- Farah successfully defended her PhD thesisCongratulations to Farah for defending her PhD thesis titled “Deep Generative Modeling-based Data Augmentation to Address the Data Scarcity Issue in Nuclear Engineering”