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