This talk will describe a class of machine learning methods for reasoning about complex physical systems. The key insight is that many systems can be represented as graphs with nodes connected by edges. I'll present a series of studies which use graph neural networks--deep neural networks that approximate functions on graphs via learned message-passing-like operations--to predict the movement of bodies in particle systems, infer hidden physical properties, control simulated robotic systems, and build physical structures. These methods are not specific to physics, however, and I'll show how we and others have applied them to broader problem domains with rich underlying structure.
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