Potentialities and limitations of machine learning to solve cut-and-shuffle mixing problems: A case study

Thomas F. Lynn, Julio M. Ottino, Richard M. Lueptow, Paul B. Umbanhowar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well studied, and a simple mixing methodology, which we refer to as the longest segment (LS) method, works well under a broad range of situations. We use supervised learning to train a neural network (NN) to emulate the LS mixing algorithm for mixing a one-dimensional domain of two species. We find that a generic deep NN can emulate the LS method with good accuracy but cannot generalize to conditions significantly outside its training repertoire. The challenges in defining the mixing problem and generalizing a ML mixing approach are indicative of those expected for more complex systems where optimal or near optimal mixing methods remain unknown.

Original languageEnglish (US)
Article number117840
JournalChemical Engineering Science
StatePublished - Oct 12 2022


  • Artificial intelligence
  • Cutting-and-shuffling
  • Granular materials
  • Interval exchange transforms
  • Machine learning
  • Mixing

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering


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