A Neuro-Symbolic Method for Solving Differential and Functional Equations

Understand

When neural networks are used to solve differential equations, they usually produce solutions in the form of black-box functions that are not directly mathematically interpretable.

• We introduce a method for generating symbolic expressions to solve differential equations while leveraging deep learning training methods.
• Unlike existing methods, our system does not require learning a language model over symbolic mathematics, making it scalable, compact, and easily adaptable for a variety of tasks and configurations.
• As part of the method, we propose a novel neural architecture for learning mathematical expressions to optimize a customizable objective.