Work

Verification and validation for trustworthy scientific machine learning

Year: 2026

Type: article

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.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ae59ec

Authors: John D Jakeman, Lorena A Barba, Joaquim R R A Martins, Thomas O’Leary-Roseberry

Institutions: Sandia National Laboratories, George Washington University, University of Michigan

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,000

Funder: National Science Foundation