Publications

This paper discusses the potential of automating knowledge discovery and incorporation in science using machine learning. It examines symbolic regression as a benchmark task, which aims to predict mathematical equations describing observational data. The paper also explores integrating known governing equations into deep neural networks to predict system behavior.

The paper aims to summarize key research works in symbolic regression and perform a comparative study to understand the strengths and limitations of each method. Finally, we highlight the challenges in the current methods and future research directions in the application of machine learning in knowledge discovery.