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:

  1. 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.
  2. Machine Learning Model Closures! In the continuity equation, the dynamics of the density (0th moment) are determined by the divergence of the current density (1st 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