Research of Monte Carlo Laboratory
Nuclear Reactor Physics
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Numerical Methods for Neutron Transport and Diffusion Analysis
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Advanced Reactor Designs and Analysis
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High Performance Computing
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AI-Enhanced Reactor Physics Methods
Our lab is pioneering next-generation reactor simulation tools by integrating artificial intelligence with reactor physics. We develop Physics-Informed Neural Network (PINN) models that solve the neutron diffusion equation to predict a reactor’s power distribution and criticality without relying on labeled data.
Our work has demonstrated that explicitly embedding physical formulas into the AI model, rather than relying on the network’s prediction alone, dramatically improves training stability, paving the way for more reliable AI-based analysis tools.
Energy & Nuclear Policy
Our research area integrates engineering-based analysis with policy studies to provide scientifically grounded, quantitative insights for national energy strategies and the advancement of nuclear policy.
Using computational modeling, scenario analysis, and cost–benefit evaluation, we optimize Korea’s future energy portfolio and determine the appropriate share of nuclear power within it. Our work accounts for the integration of renewables, fossil fuels, and energy storage technologies to propose long-term, balanced energy mix strategies.
In parallel, we conduct policy-oriented studies on Korea’s nuclear industry and regulatory governance. This includes analyzing institutional and legal frameworks, developing industry growth roadmaps, assessing nuclear safety regulations, reviewing global regulatory trends, and evaluating the policy implications of advanced nuclear technologies such as SMRs and HTGRs. Through these efforts, we aim to deliver actionable roadmaps that link policy, technology, and industry, thereby strengthening the strategic role of nuclear energy in national policy.
