Description:
Explore graph-based machine learning techniques for modeling physical structures and dynamics in this IEEE Signal Processing Society webinar. Delve into the rich structure of datasets and learn about graphical networks, algorithm explanations, and model architectures. Discover various simulations including sand, goop, particle, and multiple materials. Examine research on rigid materials, mesh-based systems, and compressible/incompressible fluids. Investigate generalization experiments, system and chemical Polygem, construction species, and the Silhouette Task. Compare absolute vs. relative action and edge-based relative agent results. Gain insights from Peter Battaglia of Deepmind in this comprehensive exploration of graph-based approaches to physical modeling.
Modeling Physical Structure and Dynamics Using Graph-Based Machine Learning
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