Fundamental AI/ML design and reliability questions for physics-based applications

Determine appropriate neural network architectures (including depth and width), quantify sufficient training data requirements, develop training approaches for highly nonconvex optimization problems, establish accuracy guarantees, and identify conditions under which Artificial Intelligence/Machine Learning methods can be trusted when applied to physics-based problems lacking explicit physics models.

Background

The report highlights that purely data-driven AI/ML approaches can fail to generalize on basic physics-based models, underscoring the need for mathematical rigor to understand and improve their performance in scientific and engineering contexts.

Within this discussion, the authors explicitly note that several foundational aspects of AI/ML—such as model architecture selection, data sufficiency, training strategies for nonconvex problems, accuracy expectations, and trustworthiness—remain unresolved, especially for physics problems formulated without explicit governing laws.