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variants of importance sampling. We will connect these methods to modern formulations of Monte Carlo algorithms to improve their accuracy, scalability, and overall computational cost. The methodology so
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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quantification and data science. Potential investigation areas: • Enhancing Monte Carlo and Markov Chain Monte Carlo (MCMC) with reinforcement learning. • Developing adaptive tuning and continual learning
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of a DG method for solving the hyperbolic BBGK equation has not yet been fully exploited by the rarefied gas dynamics community which continues to rely heavily on the costly direct simulation Monte Carlo
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the theory by putting it on a lattice. The resulting equations are solved numerically using specialized Monte Carlo techniques. There are many applications that require the solution of QCD so that the standard