Enhancing Transformer Representations of Symbolic ODE Expressions

This paper addresses the challenge of learning structured representations of symbolic ODE expressions using Transformer models. Unlike conventional approaches such as symbolic regression, PINNs, and neural operators that focus on numerical approximations or blind symbolic search, this work emphasizes preserving commutative properties in mathematical expressions. The proposed method aims to support mathematical reasoning capabilities in symbolic form rather than relying solely on fitting to numerical data.