Michael Krygier

Senior Member of Technical Staff

(505) 284-2848

Biography

Michael C. Krygier earned his Ph.D. in Physics from the Georgia Institute of Technology, where his research centered on computational fluid dynamics. His work specifically addressed the turbulence problem through the lens of dynamical systems, leading to the development of a nonlinear optimization software tool designed to calculate Exact Coherent Structures (ECSs), or unstable invariant solutions, to the Navier-Stokes equations. Prior to his doctoral studies, he obtained dual B.S. degrees in Physics and Mathematics from Georgia Southern University.

Krygier’s research interests are diverse and interdisciplinary, encompassing areas such as Bose-Einstein condensates, atom interferometers, quantum physics, quantum computing, fluid turbulence, nonlinear dynamics, hypersonics, thermodynamics, numerical optimization algorithms, software development, and high-performance computing. At the Neural Exploration & Research Laboratory (NERL) at Sandia, his current research focuses on the development of neuromorphic computing algorithms, particularly non-conventional approaches like neural random walkers that differ from traditional neural networks.

He is a key developer of Sandia’s Fugu software tool, which facilitates the creation of general neural algorithms that can be easily compiled and executed on various neuromorphic hardware platforms. This capability is particularly advantageous as it eliminates the need to implement the same neural algorithm independently for each neuromorphic system. Krygier is excited about the prospect of deploying new neuromorphic computing algorithms on state-of-the-art neuromorphic hardware, which boasts more than one-billion neurons, using Sandia’s Fugu software.

Education

Ph.D., Physics, Georgia Institute of Technology

B.S., Physics, Georgia Southern University

B.S., Mathematics, Georgia Southern University

Publications

Ryan McMullen, Michael Krygier, John Torczynski, Michail Gallis, (2022). Navier-Stokes Equations Do Not Describe the Smallest Scales of Turbulence in Gases Physical Review Letters https://doi.org/10.1103/PhysRevLett.128.114501 Publication ID: 80536

Ryan McMullen, Michael Krygier, John Torczynski, Michail Gallis, (2022). Gas-kinetic simulations of compressible turbulence over a mean-free-path-scale porous wall AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022 https://doi.org/10.2514/6.2022-1058 Publication ID: 76996

Michael Krygier, Tyler LaBonte, Carianne Martinez, Chance Norris, Krish Sharma, Lincoln Collins, Partha Mukherjee, Scott Roberts, (2021). Quantifying the unknown impact of segmentation uncertainty on image-based simulations Nature Communications https://doi.org/10.1038/s41467-021-25493-8 Publication ID: 75373

Michael Krygier, John Torczynski, Michail Gallis, (2021). Exploring near-continuum turbulent compressible flow in the Taylor-Green vortex https://doi.org/10.2172/1897023 Publication ID: 76633

Scott Roberts, Brendan Donohoe, Carianne Martinez, Michael Krygier, Bernadette Hernandez-Sanchez, Collin Foster, Lincoln Collins, Benjamin Greene, David Noble, Chance Norris, Kevin Potter, Christine Roberts, Kyle Neal, Sylvain Bernard, Benjamin Schroeder, Bradley Trembacki, Tyler LaBonte, Krish Sharma, Tyler Ganter, Jessica Jones, Matthew Smith, (2021). Credible, Automated Meshing of Images (CAMI) https://www.osti.gov/servlets/purl/1900115 Publication ID: 76941

Michael Krygier, Tyler LaBonte, Carianne Martinez, Chance Norris, Krish Sharma, Lincoln Collins, Partha Mukherjee, (2021). Quantifying the Unknown: Impact of Segmentation Uncertainty on Image-based Simulations https://doi.org/10.2172/1884065 Publication ID: 79489

Michael Krygier, Tyler LaBonte, Carianne Martinez, Chance Norris, Krish Sharma, Lincoln Collins, Partha Mukherjee, Scott Roberts, (2020). Quantifying the Unknown: Impact of Segmentation Uncertainty on Image-based Simulations https://doi.org/10.1038/s41467-021-25493-8 Publication ID: 72155

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