Session

DescriptionAs exascale computing becomes more prominent, alongside AI-driven models and heterogeneous architectures, the nature of scientific discovery is evolving, and with it comes our responsibility to ensure its trustworthiness. This panel addresses shifts driven by scale, the balance between physics-based simulations and machine-learned surrogates, and the growing complexity of computational workflows. Can verification, validation, and uncertainty quantification (VVUQ) keep pace with non-deterministic hardware, low-precision computation, and workflows/volumes that are increasingly difficult to reproduce? As AI co-authors discovery, how do we ensure accountability in science? Beyond the technical domain, the panel explores HPC’s role in shaping societal trust. As scientific communication competes for attention in digital spaces, how can the community ensure that its outputs remain credible, transparent, and accessible? The panel highlights inclusive co-design as a promising path forward; bringing together diverse stakeholders across disciplines, sectors, and society to embed trust and shared ownership into the foundations of research.