Generating Elementary Integrable Expressions

Rashid Barket, Matthew England, Juergen Gerhard

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review


There has been an increasing number of applications of machine learning to the field of Computer Algebra in recent years, including to the prominent sub-field of Symbolic Integration. However, machine learning models require an abundance of data for them to be successful and there exist few benchmarks on the scale required. While methods to generate new data already exist, they are flawed in several ways which may lead to bias in machine learning models trained upon them. In this paper, we describe how to use the Risch Algorithm for symbolic integration to create a dataset of elementary integrable expressions. Further, we show that data generated this way alleviates some of the flaws found in earlier methods.
Original languageEnglish
Title of host publicationComputer Algebra in Scientific Computing
Subtitle of host publication25th International Workshop, CASC 2023, Havana, Cuba, August 28 – September 1, 2023, Proceedings
EditorsFrançois Boulier, Matthew England, Ilias Kotsireas, Timur M. Sadykov, Evgenii V. Vorozhtsov
Number of pages18
ISBN (Electronic)978-3-031-41724-5
ISBN (Print)978-3-031-41723-8
Publication statusPublished - 26 Sept 2023
Event25th International Workshop on Computer Algebra in Scientific Computing - Cuba, Havana, Cuba
Duration: 28 Aug 20231 Sept 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Workshop on Computer Algebra in Scientific Computing
Abbreviated titleCASC 2023
Internet address


  • Computer Algebra
  • Symbolic Integration
  • Machine Learning
  • Data Generation


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