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.
22 June, 2022 AMD donates an MI200-series GPU server to the group!
9 June, 2022 Ph.D. student Fatima Chrit and incoming freshman Sriharsha Kocherla have their first conference abstract on quantum algorithms for fluid flows accepted to TQC 2022!
28 May, 2022 Spencer wins Powe Junior Faculty Enhancement Award from Oak Ridge National Lab.
14 May, 2022 Group wins GT Seed Grant for quantum simulation!
25 Apr, 2022 Congratulations to Qi Zeng on his first conference paper, which was accepted to an ICLR workshop! It’s arXived here.