Bethany Lusch - Scalable graph-based surrogate models for unstructured meshes - IPAM at UCLA

Recorded 18 May 2026. Bethany Lusch of Argonne National Laboratory presents "Scalable graph-based surrogate models for unstructured meshes" at IPAM's Multi-Fidelity Methods to Enable Robust Optimization and Real-Time Control of Fusion Processes Workshop.
Abstract: To train a neural network as a surrogate model for simulations on an unstructured mesh, such as computational fluid dynamics (CFD) simulations, it is helpful to maintain the structure of the mesh, rather than interpolating onto a regular grid. We describe our recent work using graph neural networks and graph transformers for unstructured mesh data. Large-scale simulations on supercomputers often involve meshes that do not fit on one processor and are split with a domain decomposition. We maintain the same mesh partitioning while training the neural network, enforcing "physical consistency" via halo nodes. This work is demonstrated using data from NekRS, a spectral-element CFD solver, and we demonstrate efficient scaling of training across many GPUs.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-multi-fidelity-methods-to-enable-robust-optimization-and-real-time-control-of-fusion-processes/?tab=overview Receive SMS online on sms24.me

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