About

Spencer Bryngelson is a tenure-track assistant professor in the College of Computing at Georgia Tech. His research areas include computational physics, numerical methods, fluid dynamics, and high performance computing. Previously, he was a senior postdoctoral researcher at Caltech, working with Professor Tim Colonius. He has also been a visiting researcher at MIT with Professor Themis Sapsis and a postdoctoral researcher at the Center for Exascale Simulation of Plasma-Coupled Combustion (XPACC). Bryngelson received his Ph.D. and M.S. in Theoretical and Applied Mechanics from the University of Illinois at Urbana–Champaign in 2017 and 2015, respectively, working with Professor Jonathan Freund. He earned B.S. degrees in Mechanical Engineering and Mathematics from the University of Michigan–Dearborn in 2013.

Research topics

• Computer science
• Mechanics
• Physics
• Statistical physics
• Mathematics

Selected publications

• Shocks without shock capturing: Information geometric regularization of finite volume methods for Navier–Stokes-like problems

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Fourth-Order HyQMOM Closures for Multidimensional Kinetic Equations

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• MFC 5.0: An exascale many-physics flow solver

  Mendeley Data · 2026-02-26

  datasetOpen accessSenior author

  Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. We exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions.

  PublisherDOI

• A Symbolic Computational Abstraction of Chemistry Libraries

  2026-01-08

  articleSenior author

  Accurate simulations of reacting flows require computational representations of chemical source terms. Dedicated libraries can provide such representations by acting as interfaces between mechanistic data and flow solvers. However, libraries form a fragmented computational landscape, with each library typically implementing kinetic models for different applications (e.g., combustion, plasma, or nuclear reactions). Furthermore, libraries impose rigid data layout constraints and must be compiled before linking with the flow solver. These issues complicate the simulations of multi-kinetic flows (e.g., plasma-coupled combustion) and hinder the automatic differentiation (AD) and joint optimization of flow solvers on modern hardware, such as GPUs. We develop a symbolic representation of chemistry that addresses these issues. This representation uses the general structure of mass-action kinetics, making the system agnostic to the underlying library. Chemical and computational details are progressively added using domain-specific library data to create an application-specific computational model. The computational model is well-suited for generating code for accelerator devices. We demonstrate this property by using three different libraries to compute combustion, plasma, and nuclear kinetics on GPU devices.

  PublisherDOI

• Shocks without shock capturing: Information geometric regularization of finite volume methods for Navier--Stokes-like problems

  arXiv (Cornell University) · 2026-04-08

  preprintOpen access

  Shock waves in high-speed fluid dynamics produce near-discontinuities in the fluid momentum, density, and energy. Most contemporary works use artificial viscosity or limiters as numerical mitigation of the Gibbs--Runge oscillations that result from traditional numerics. These approaches face a delicate balance in achieving sufficiently regular solutions without dissipating fine-scale features, such as turbulence or acoustics. Recent work by Cao and Schäfer introduces information geometric regularization (IGR), the first inviscid regularization method for fluid dynamics. IGR replaces shock singularities with smooth profiles of adjustable width, without dissipating fine-scale features. This work provides a strategy for the practical use of IGR in finite-volume-based numerical methods. We illustrate its performance on canonical test problems and compare it against established approaches based on limiters and Riemann solvers. Results show that the finite volume IGR approach recovers the expected solutions in all cases. Across canonical benchmarks, IGR achieves accuracy competitive with WENO and LAD shock-capturing schemes in both smooth and discontinuous flow regimes. The IGR approach is computationally light, with meaningfully fewer memory accesses and arithmetic operations per time step.

  PublisherDOI

• Direction Numerical Simulation Data: 2D Acoustic Slit

  Open MIND · 2026-02-12

  datasetOpen accessSenior author
  OA PDFDOI

• MFC 5.0: An exascale many-physics flow solver

  Computer Physics Communications · 2026-01-30 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• Shocks without shock capturing: Information geometric regularization of finite volume methods for Navier--Stokes-like problems

  arXiv (Cornell University) · 2026-04-08

  articleOpen access

  Shock waves in high-speed fluid dynamics produce near-discontinuities in the fluid momentum, density, and energy. Most contemporary works use artificial viscosity or limiters as numerical mitigation of the Gibbs--Runge oscillations that result from traditional numerics. These approaches face a delicate balance in achieving sufficiently regular solutions without dissipating fine-scale features, such as turbulence or acoustics. Recent work by Cao and Schäfer introduces information geometric regularization (IGR), the first inviscid regularization method for fluid dynamics. IGR replaces shock singularities with smooth profiles of adjustable width, without dissipating fine-scale features. This work provides a strategy for the practical use of IGR in finite-volume-based numerical methods. We illustrate its performance on canonical test problems and compare it against established approaches based on limiters and Riemann solvers. Results show that the finite volume IGR approach recovers the expected solutions in all cases. Across canonical benchmarks, IGR achieves accuracy competitive with WENO and LAD shock-capturing schemes in both smooth and discontinuous flow regimes. The IGR approach is computationally light, with meaningfully fewer memory accesses and arithmetic operations per time step.

  PublisherOA PDF

• Pyrometheus: Symbolic abstractions for XPU and automatically differentiated computation of combustion kinetics and thermodynamics

  ArXiv.org · 2025-03-31

  preprintOpen access

  The cost of combustion simulations is often dominated by the evaluation of net production rates of chemical species and mixture thermodynamics (thermochemistry). Execution on computing accelerators (XPUs) like graphic processing units (GPUs) can greatly reduce this cost. However, established thermochemistry software is not readily portable to such devices or sacrifices valuable analytical forms that enable differentiation for sensitivity analysis and implicit time integration. Symbolic abstractions are developed with corresponding transformations that enable computation on accelerators and automatic differentiation by avoiding premature specification of detail. The software package Pyrometheus is introduced as an implementation of these abstractions and their transformations for combustion thermochemistry. The formulation facilitates code generation from the symbolic representation of a specific thermochemical mechanism in multiple target languages, including Python, C++, and Fortran. Computational concerns are separated: the generated code processes array-valued expressions but does not specify their semantics. These semantics are provided by compatible array libraries, such as NumPy, Pytato, and Google JAX. Thus, the generated code retains a symbolic representation of the thermochemistry, which translates to computation on accelerators and CPUs and automatic differentiation. The design and operation of these symbolic abstractions and their companion tool, Pyrometheus, are discussed throughout. Roofline demonstrations show that the computation of chemical source terms within MFC, a Fortran-based flow solver we link to Pyrometheus, is performant.

  PublisherOA PDFDOI

• A multiple-circuit approach to quantum resource reduction with application to the quantum lattice Boltzmann method

  Future Generation Computer Systems · 2025-07-01 · 6 citations

  articleSenior authorCorresponding
  PublisherDOI

Frequent coauthors

• Henry Le Berre

  Georgia Institute of Technology

  85 shared

• Sebastian Keller

  ETH Zurich

  83 shared

• Paul R. C. Kent

  Oak Ridge National Laboratory

  82 shared

• Osman Seckin Simsek

  University of Basel

  82 shared

• Ross Miller

  Oak Ridge National Laboratory

  82 shared

• J. Austin Harris

  82 shared

• Wael Elwasif

  Oak Ridge National Laboratory

  82 shared

• Óscar Hernández

  82 shared

Education

• Ph.D. Theoretical and Applied Mechanics, Mechanical Science and Engineering

  University of Illinois at Urbana-Champaign

  2017

• M.S. Theoretical and Applied Mechanics, Mechanical Science and Engineering

  University of Illinois at Urbana-Champaign

  2015

• Graduate Degree Certificate - Computational Science and Engineering, Computational Science and Engineering

  University of Illinois at Urbana-Champaign

  2014

• B.S. Mechanical Engineering

  University of Michigan Dearborn

  2013

• B.S. Engineering Mathematics

  University of Michigan Dearborn

  2013