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Exploring the Role of Machine Learning in Scientific Workflows: Opportunities and Challenges

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arxiv 2110.13999 v1 pith:TXEXBJWV submitted 2021-10-26 cs.DC

Azita Nouri , Philip E. Davis , Pradeep Subedi , Manish Parashar This is my paper · ORCID
classification cs.DC
keywords challengesworkflowstechniquesexecutionscientificdiscussin-situlearning
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In this survey, we discuss the challenges of executing scientific workflows as well as existing Machine Learning (ML) techniques to alleviate those challenges. We provide the context and motivation for applying ML to each step of the execution of these workflows. Furthermore, we provide recommendations on how to extend ML techniques to unresolved challenges in the execution of scientific workflows. Moreover, we discuss the possibility of using ML techniques for in-situ operations. We explore the challenges of in-situ workflows and provide suggestions for improving the performance of their execution using ML techniques.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science

    cs.AI 2025-09 conditional novelty 5.0 of 10

    Scientific workflows and AI agents are unified under a state machine abstraction, yielding a 5x5 evolution matrix from static pipelines to swarms of intelligent agents.

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