My name is Thomas Chuna. I go by TC.

Life has brought me through many different research fields since I began my research career. I started in pure mathematics where I studied matrix representations of mapping class groups (here) From 2013-2016, I worked in experimental nuclear physics. I simulated radiation transport on Wittenberg university’s computing cluster (here). Additionally, I helped run the high precision beta decay experiment at Michigan State’s National Superconducting Cyclotron in 2016 (here). Following this, I entered a research position in Lattice Quantum Chromodynamics. I ran Markov Chain Monte Carlo (MCMC) simulations of SU(3) gauge fields on anisotropic spacetime grids at Michigan State’s high performance computing cluster ICER. Accompanying these MCMC simulations, I developed/implemented efficient anisotropic Lie group integrators (here). These integrators were used in conjunction with time series analysis techniques for renormalization/scale setting.

In 2021, I entered a research position with the Murillo Lab! Here I have been studying a host of topics

**Ongoing Research:**

- Hybrid Models! At its simplest, a hybrid model is a one which stitches together 2 separate models (model A and B) and runs them simultaneously. Consider a gaseous and kinetic tritium-hydrogen pore surrounded by an equilibrated Carbon-Deuterium foam. A hybrid model can simulate the gaseous pore using a kinetic model and the foam using a hydrodynamic model. Kinetic models provide more degrees of freedom (DoF) than a hydrodynamics model, but the additional DoF raise computational cost. So using the hydrodynamics model where the additional DoF aren’t necessary can save computational cost and preserve model Fidelity.
- Machine Learning Model Closures! In the continuity equation, the dynamics of the density (0
^{th}moment) are determined by the divergence of the current density (1^{st}moment). In general, the dynamics of the nth moment require the (n+1)th moment. This collection of n equations has n+1 unknowns. To solve the system of equations, the n+1 moment must be rewritten in terms of the previous n variables. This is constraint is called a model closure. Recently there has been interest in having neural nets predict the n+1 moment using the other n moments (i.e. use a NN closure). This allows optimization techniques to determine the form of the closure.

**Education:**

- BSc, Physics and Mathematics with Computational Science and Computer Science minors, 2012 – 2016

Wittenberg University, United States - MSc, Physics, 2016 – Present

Michigan State University, United States - PhD, Physics and Computational Mathematics, Science and Engineering, 2016 – Present

Michigan State University, United States

**Publications:**

**Contact:**

chunatho@msu.edu