The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)

Understand

Lample and Charton (2019) describe a system that uses deep learning technology to compute symbolic, indefinite integrals, and to find symbolic solutions to first- and second-order ordinary differential equations, when the solutions are elementary functions.

• They found that, over a particular test set, the system could find solutions more successfully than sophisticated packages for symbolic mathematics such as Mathematica run with a long time-out.
• This is an impressive accomplishment, as far as it goes.
• However, the system can handle only a quite limited subset of the problems that Mathematica deals with, and the test set has significant built-in biases.