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Accelerating Particle and Fluid Simulations with Differentiable and Interpretable Graph Networks for Solving Forward and Inverse Problems
DescriptionWe leverage physics-embedded differentiable graph network simulators (GNS) to accelerate particulate and fluid simulations to solve forward and inverse problems. GNS represents the domain as a graph with particles as nodes and learned interactions as edges, improving generalization to new environments. GNS achieves over 165x speedup for granular flow prediction compared to parallel CPU simulations. We propose a novel hybrid GNS/Material Point Method to accelerate forward simulations by minimizing error on a surrogate model, achieving 24x speedup. The differentiable GNS enables solving inverse problems through automatic differentiation, identifying material parameters that result in target runout distances. We demonstrate solving inverse problems by iteratively updating the friction angle by computing the gradient of a loss function based on the final and target runouts, thereby identifying the friction angle that matches the observed runout. The physics-embedded and differentiable simulators open an exciting paradigm for AI-accelerated design, control, and optimization.
Event Type
Workshop
TimeMonday, 13 November 20232pm - 2:20pm MST
Location501-502
Artificial Intelligence/Machine Learning
W