mlsanityData to 14 Oct 2026

Verification and Validation for Trustworthy Scientific Machine Learning

arXiv 2502.15496 ↗First posted 21 Feb 2025cs.LGcited by 4

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Abstract (arXiv)

Scientific machine learning (SciML) models are transforming many scientific disciplines. However, the development of good modeling practices to increase the trustworthiness of SciML has lagged behind its application, limiting its potential impact. The goal of this paper is to start a discussion on establishing consensus-based good practices for predictive SciML. We identify key challenges in applying existing computational science and engineering guidelines, such as verification and validation protocols, and provide recommendations to address these challenges. Our discussion focuses on predictive SciML, which uses machine learning models to learn, improve, and accelerate numerical simulations of physical systems. While centered on predictive applications, our 16 recommendations aim to help researchers conduct and document their modeling processes rigorously across all SciML domains.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 44 of its 133 references that are in the map. First posted on arXiv 21 Feb 2025.

Builds on

44 of 133 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEscited by 2
  2. 2026Energy-efficient operation of neural operators for virtual sensingcited by 1
  3. 2026Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operatorscited by 0
  4. 2026A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturingcited by 0