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. We are the Computational Physics Group at Georgia Tech CSE and we develop state-of-the-art computational models and numerical methods to push these applications forward. Formulations leverage domain expertise in physics and biology and data-driven tools like machine learning and data assimilation. Our open-source scientific software utilizes these methods and scales to the world’s largest computers. Check out the links above to learn more, or visit this page if you’re interested in joining our group.
Bubble cavitation can ablate kidney stones, but wreaks havoc on marine propellers. We developed a data-driven sub-grid method for simulating this phenomenon. It utilizes a LSTM recurrent neural network to close the governing equations at low cost. MFC, our open-source multi-phase flow solver, demonstrates this method. MFC is also capable of fully-resolved multi-phase fluid dynamics via the diffuse-interface method.
The spectral boundary integral method leads to high-fidelity prediction and analysis of blood cells transitioning to chaos in a microfluidic device (above). We developed a low-order model for the cell-scale flow, important for guiding microfluidic device design and improving treatment outcomes.
25 Apr, 2022 Congratulations to Qi Zeng on his first conference paper, which was accepted to an ICLR workshop! It’s arXived here.
13 Apr, 2022 Undergraduates Ajay Bati and Qi Zeng gave great talks at the Annual GT Undergraduate Research Symposium, well done!
26 Mar, 2022 Spencer visits Brown, MIT, and Harvard. Thanks to Professors Rodriguez, Sapsis, and Koumoutsakos for hosting!
1 Mar, 2022 Ajay Bati joins our group. He’s working on deploying neural network models in HPC settings!
21 Feb, 2022 GSCS22 was this past Saturday! It was sponsored by COC CSE.