Cody J. Permann

Chaitanya Bhave, Pierre-Clément A. Simon, Casey Icenhour et al.

The widespread adoption of AI-assisted development in scientific software is not a future concern -- it is a present reality. Researchers are already using large language models to write code, generate test cases, and draft documentation, yet this practice remains largely unacknowledged and unguided in formal workflows and published work. This ad hoc, ungoverned use of AI represents a systemic risk to scientific software quality, particularly in safety-relevant modeling and simulation tools subject to strict Software Quality Assurance (SQA), or even Nuclear Quality Assurance Level 1 (NQA-1) standards, for which traceability, independent verification, and documented procedures are paramount. The question facing the scientific software community is, therefore, not whether to permit AI-assisted development, but how to govern it responsibly. This paper proposes guidance for AI-assisted code development in the context of strict software quality assurance. Using TMAP8 -- an open-source tritium migration code for fusion energy -- as a demonstration platform, we propose a structured framework for AI-assisted verification and validation (V&V) case development. V&V case development represents the ideal proving ground for establishing that governance: because validation cases have known solutions, correctness is objectively measurable, errors are identifiable by design, and the artifacts are fully auditable. The proposed guidance, developed based on practical experience described herein, operates within NQA-1 requirements, preserves human accountability, and establishes the disclosure and review standards that responsible AI-assisted scientific software development demands.

Fande Kong, Yaqi Wang, Derek R. Gaston et al.

The multigroup neutron transport equations have been widely used to study the motion of neutrons and their interactions with the background materials. Numerical simulation of the multigroup neutron transport equations is computationally challenging because the equations is defined on a high dimensional phase space (1D in energy, 2D in angle, and 3D in spatial space), and furthermore, for realistic applications, the computational spatial domain is complex and the materials are heterogeneous. The multilevel domain decomposition methods is one of the most popular algorithms for solving the multigroup neutron transport equations, but the construction of coarse spaces is expensive and often not strongly scalable when the number of processor cores is large. In this paper, we study a highly parallel multilevel Newton-Krylov-Schwarz method equipped with several novel components, such as subspace-based coarsening, partition-based balancing and hierarchical mesh partitioning, that enable the overall simulation strongly scalable in terms of the compute time. Compared with the traditional coarsening method, the subspace-based coarsening algorithm significantly reduces the cost of the preconditioner setup that is often unscalable. In addition, the partition-based balancing strategy enhances the parallel efficiency of the overall solver by assigning a nearly-equal amount of work to each processor core. The hierarchical mesh partitioning is able to generate a large number of subdomains and meanwhile minimizes the off-node communication. We numerically show that the proposed algorithm is scalable with more than 10,000 processor cores for a realistic application with a few billions unknowns on 3D unstructured meshes.