  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.
