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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
- Farah passed her PhD Prelim ExamFarah successfully passed her PhD Preliminary Exam with a title “Deep Generative Modeling-based Data Augmentation to Address the Data Scarcity Issue in Nuclear Engineering “….
- Ziyu’s paper accepted by International Journal of Heat and Mass TransferZiyu’s paper titled “Hierarchical Bayesian Modeling for Inverse Uncertainty Quantification of System Thermal-Hydraulics Code using Critical Flow Experimental Data” has been accepted for publication at…
- Aidan passed his PhD Qualification ExamAidan Furlong successfully passed his PhD Qualification Exam part 2 defense. Congratulations Aidan!
- Lauren selected by OECD NEA Global Forum Rising Stars ProgrammeLauren has been selected by the OECD NEA Global Forum Rising Stars Programme. She will present her research work at the NEA Global Forum Rising…
- Ziyu defended his PhD thesisZiyu successfully defended his PhD thesis titled “Machine Learning-based Model Discrepancy in Bayesian Inverse Uncertainty Quantification”. Congratulations Ziyu!
- Aidan won the 2nd place at the 2024 DOE Cybersecurity Conference poster contestAidan won the 2nd place in the 2024 DOE Cybersecurity and Technology Innovation Conference student poster contest. The title of his poster is “Hybrid and…
- Dr. Wu to participate in new DOE NNSA university consortiumThe U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) Office of Defense Nuclear Nonproliferation has awarded $25 million to the “Enabling Capabilities in Technology…
- Chris and Sofiia attended the 2024 DOE NNSA UPR MeetingChris and Sofiia attended the 2024 DOE National Nuclear Security Administration (NNSA) University Program Review (UPR) Meeting at Texas A&M University, College Station, TX during…
- Farah, Alie and Lauren presented at the BEPU Conference in Lucca ItalyPhD students Farah, Alie, and undergraduate student (incoming PhD) Lauren attended the 2024 Best Estimate Plus Uncertainty International Conference (BEPU 2024) at Lucca, Italy during…