Date: Wednesday 03-Apr-2024
Time: 11:30-12:30 pm
Location: PSC 1136
Speaker: Dongwook Lee (University of California Santa Cruz)
Title: Enhancing numerical algorithms and their applications for predictive astro-fluid dynamics simulations
Abstract: We introduce a groundbreaking advancement in computational fluid dynamics: the GP-MOOD method, a cutting-edge shock-capturing high-order algorithm. This innovative approach leverages Gaussian Processes (GP) to simulate compressible, shock-dominant fluid flows. Specifically, it incorporates the Multidimensional Optimal Order Detection (MOOD) strategy to effectively manage solutions at shocks and discontinuities. MOOD not only enhances numerical stability but also ensures precision through an adaptive shock-capturing framework. Expanding on this foundation, we delve into our pioneering integration of machine learning within the GP-MOOD framework. By employing a neural network (NN) to discern shock patterns, our NN-GP-MOOD approach achieves an unparalleled equilibrium between high-order accuracy and stability, dynamically adjusting to local flow conditions. This sophisticated strategy optimizes numerical accuracy and stability while maintaining the overall predictive capabilities in simulating shock-dominant flows.
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Special accommodations for individuals with disabilities can be made by calling (301) 405-3001. It would be appreciated if we are notified at least one week in advance.
