Skip to yearly menu bar Skip to main content


Learning advanced mathematical computations from examples

Fran├žois Charton · Amaury Hayat · Guillaume Lample


Keywords: [ computation ] [ differential equations ] [ transformers ] [ deep learning ]


Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative characteristics, and good approximations of numerical features of the system. This demonstrates that neural networks can learn to perform complex computations, grounded in advanced theory, from examples, without built-in mathematical knowledge.

Chat is not available.