@inproceedings{
sharma2023sird,
title={{SIRD}: Symbolic Integration Rules Dataset},
author={Vaibhav Sharma and abhinav nagpal and Muhammed Fatih Balin},
booktitle={The 3rd Workshop on Mathematical Reasoning and AI at NeurIPS'23},
year={2023},
url={https://openreview.net/forum?id=WWDsbsgyhS}
}
Advancements in neural networks and computer hardware lead to new use cases for deep learning in the natural sciences every day. Even though symbolic 
mathematics tasks have been explored, symbolic integration only has a few studies using black box models and currently lacks explainability. Symbolic integration 
is a challenging search problem and the final result is obtained by applying different integration rules at each step. We propose a novel and interpretable approach to perform symbolic integration using deep learning through integral rule prediction to speed up the search. We introduce the first-of-its-kind symbolic integration rules dataset comprising two million distinct functions and integration rule pairs. For complex rules 
such as u-substitution and integration by parts, it also includes the expression needed for rule application.
We also train a transformer model on our proposed dataset and incorporate it into 
SymPy's integral\_steps function to get guided\_integral\_steps, resulting in $6\times$ fewer branches explored by allowing our model to guide the depth-first-search procedure.
SIRD: Symbolic Integration Rules Dataset
Vaibhav Sharma abhinav nagpal Muhammed Fatih Balin