Verification and Validation for Trustworthy Scientific Machine Learning

TL;DR

This paper discusses establishing consensus-based verification and validation practices to enhance trustworthiness in scientific machine learning, addressing key challenges and providing 16 recommendations for rigorous modeling.

Contribution

It introduces a set of 16 recommendations to improve verification and validation practices in predictive SciML, fostering more trustworthy scientific models.

Findings

Identifies key challenges in applying existing verification and validation protocols to SciML.

Provides 16 actionable recommendations for rigorous modeling and documentation.

Highlights the importance of consensus-based good practices in SciML.

Abstract

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.