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, 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.
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.
Fatima Chrit (with A. Alexeev in ME) and Jesus Arias (with L. Sankar in AE) join the group! They are funded via GTQA and GTRI.4 Oct, 2021
Joint work with MIT SAND group on stable high-order QBMMs is now on arXiv!14 Sept, 2021
Girish Ganesan (GPU computing) and Sriharsha Kocherla (quantum algorithms) joined the group!