Abstract Symbolic regression, a key problem in discovering physics formulas from observational data, faces persistent challenges in scalability and interpretability. We introduce PhyE2E, an AI framework designed to discover physically meaningful symbolic expressions. PhyE2E decomposes the symbolic regression problem into subproblems via second-order neural network derivatives, and employs a transformer architecture to translate data into symbolic formulas in an end-to-end manner.