Welcome!
Nano-scale transistors fill warehouse-scale supercomputers, yet their performance still constrains development of the jets that defend us, the medical therapies our lives depend upon, and the renewable energy sources that will power our generation into the next. The Computational Physics Group at Georgia Tech develops computational models and numerical methods to push these applications forward. We accompany our methods with algorithms crafted to make efficient use of the latest exascale machines and computer architectures, including AMD GPUs, Arm/RISC CPUs, and quantum computers. We develop open-source software for these methods that scales to the world’s largest supercomputers. Check out the rest of this website to learn more.
Openings? Visit this page if you’re interested in joining our group.
Examples of our work
Bubble cavitation and droplet shedding are fundamental multiphase flow problems at the core of naval hydrodynamics, aerospace propulsion, and more. We developed a sub-grid method for simulating these phenomena. MFC, our open-source exascale-capable multi-phase flow solver, demonstrates such scale-resolving simulation of a shock-droplet interaction in the above video (via Ph.D. student Ben Wilfong).
The spectral boundary integral method leads to high-fidelity prediction and analysis of blood cells transitioning to chaos in a microfluidic device. This method of simulation provides resolution of strong cell membrane deformation with scant computational resources. We developed a stochastic model for the cell-scale flow, enabling microfluidic device design and improving treatment outcomes. The video above shows a microaneurysm (simulated by Suzan Manasreh).
News
1 April, 2025 Our work on symbolic expressions and transversal for accelerated and differentiable reacting flow simulation is on arXiv! The method is implemented in an open source package called Pyrometheus and has been linked into MFC.
17 March 2025 We are at the APS March Meeting talking about exascale compressible flow simulation on El Capitan and GPU-based compact finite difference algorithms!
11 March, 2025 MFC 5.0 appears in code and as a preprint manuscript on arXiv! MFC 5.0 is a many-physics multiphase compressible flow solver that scales ideally on exascale machines, including LLNL El Capitan and OLCF Frontier. Read more about the latest features at the manuscript link.
5 March, 2025 We are at SIAM CSE in Fort Worth, Texas this week! Postdoc Tianyi Chu is presenting his work on Bayesian optimal design for discovery of soft material properties.
11 February, 2025 An article on the work of our group and our collaborators on new exascale computers, including OLCF Frontier (mostly) and now LLNL El Capitan, has been published by the Oak Ridge Leadership Computing Facility.
11 February, 2025 Today Spencer gives a Seminar at the Institute of Computational Engineering at the University of Florida! Thanks to Bala for the invitation!
10 February, 2025 Dr. Tianyi Chu’s paper on optimal discovery of soft material properties via targeted deformations, in collaboration with Jon Estrada of Michigan, was published in Computation Mechanics!
28 January, 2025 Spencer is at PNNL, a DOE lab., this week. He is giving a seminar on solving PDEs with quantum devices. Thank you Xianyu Li and Johannes Muelmenstaedt for the kind invitation!
27 January, 2025 In exascale supercomputing news, our group was granted a DAT (dedicated access time) for 100% of LLNL El Capitan (40K+ GPUs) for one day. This time was used to measure the performance of MFC, our flagship open source solver, and evaluate the readiness of the El Capitan system before it moves to the classified network. Further, our OLCF Frontier allocation was renewed into FY25. Our work with the JSC JUPITER team continues, who manage Europe’s first exascale supercomputer and early access systems.
8 January, 2025 The pIMR manuscript appears on arXiv! We devise a parsimonious scheme for high-fidelity material inference that cuts the cost of experimentation or simulation by two orders of magnitude. With this, we enable material characterization in tractable time. In collaboration with Michigan, Brown, and UT Austin.
