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.

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