Numerical simulations are essential when real-world experiments are too costly, dangerous, or plain impossible. Using computational physics we create a "digital laboratory" where we can test theories and understand complex systems without the real-world risks. For example, simulating space events helps us understand astrophysical phenomena without actually leaving Earth. At its foundation, numerical simulation converts physical events into mathematical models. Using computational fluid dynamics (CFD) as our primary tool, we manipulate these models to replicate and predict real-world behaviors.

Our research groups work on a variety of challenges related to aerospace, machine learning, high-performance computing (HPC), and simulations. We focus on two main areas: Numerical Simulation and Physics-Informed Machine Learning. In numerical simulation, we work on the mathematics and physics that is used in the design of these simulations as well as the computer science ideas for developing codes to run on HPC clusters. In Physics-Informed Machine Learning we investigate how ML/AI can enhance and accelerate simulation processes. 

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