GRAPH NEURAL NETWORKS REPRESENTING PHYSICAL SYSTEMS

A graph neural network system implementing a learnable physics engine for understanding and controlling a physical system. The physical system is considered to be composed of bodies coupled by joints and is represented by static and dynamic graphs. A graph processing neural network processes an inpu...

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Bibliographic Details
Main Authors HADSELL, Raia Thais, SPRINGENBERG, Jost, RIEDMILLER, Martin, SANCHEZ, Alvaro, HEESS, Nicolas Manfred Otto, BATTAGLIA, Peter William, MEREL, Joshua
Format Patent
LanguageEnglish
French
German
Published 18.09.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A graph neural network system implementing a learnable physics engine for understanding and controlling a physical system. The physical system is considered to be composed of bodies coupled by joints and is represented by static and dynamic graphs. A graph processing neural network processes an input graph e.g. the static and dynamic graphs, to provide an output graph, e.g. a predicted dynamic graph. The graph processing neural network is differentiable and may be used for control and/or reinforcement learning. The trained graph neural network system can be applied to physical systems with similar but new graph structures (zero-shot learning).
Bibliography:Application Number: EP20190718138