The VECMA (Verified Exascale Computing for Multiscale Applications) project played a foundational role in the development of SEAVEA by establishing the core tools, methods, and theoretical frameworks for verification, validation, and uncertainty quantification (VVUQ) in multiscale and high-performance computing applications. Through tools such as EasyVVUQ, FabSim3, QCG-PilotJob, and MUSCLE3, VECMA provided a modular and extensible software ecosystem that SEAVEA could build upon and adapt for exascale environments. VECMA’s application of these tools to real-world scientific domains—including epidemiology, fusion energy, and climate modelling—demonstrated their practical value and helped shape SEAVEA’s focus on adaptive and automated VVUQ workflows. Furthermore, VECMA fostered a strong community of experts and established best practices that SEAVEA inherited and expanded, marking a clear progression from foundational research to scalable, production-ready solutions for exascale computing.

The HiDALGO project significantly contributed to the development of SEAVEA by advancing the integration of HPC, big data analytics, and complex system simulations—capabilities that SEAVEA leverages and extends for exascale-ready VVUQ workflows. HiDALGO demonstrated how to simulate and analyze global challenges such as urban air pollution and refugee migration using high-performance computing, pushing the boundaries of scalable, data-driven modelling. This experience informed SEAVEA’s approach to coupling computational models with real-world data and managing large-scale, heterogeneous workflows. Additionally, HiDALGO’s emphasis on co-design between applications and HPC technologies helped shape SEAVEA’s architecture, ensuring that its tools remain adaptable and efficient across diverse exascale platforms. Through this, HiDALGO provided both practical insights and technical groundwork that enhanced SEAVEA’s ability to deliver robust, adaptive uncertainty quantification at scale.

The STAMINA project strengthened SEAVEA by demonstrating how uncertainty quantification (UQ) can be applied effectively in pandemic modeling, notably on COVID‑19 spread, using real-world agent-based simulations. SEAVEA integrated STAMINA’s practical UQ approaches—such as Sensitivity Analysis (SA) workflows—into its toolkit, enabling new capabilities for epidemic forecasting. Specifically, SEAVEA’s collaboration on the STAMINA COVID‑19 modeling campaigns at Brunel University showcased how EasyVVUQ-driven sensitivity analysis of geographical-pandemic models could be adapted from STAMINA to help quantify and interpret uncertainties in COVID‑19 simulations. This integration enriched SEAVEA’s portfolio, adding real-time pandemic resilience and decision-support experience to its broader exascale VVUQ framework.

The SEAVEA project was significantly advanced through funding from UKRI’s Engineering and Physical Sciences Research Council (EPSRC) under grant EP/W007711/1, which provided essential support for its development between 2021 and 2024. This grant enabled the creation and refinement of the SEAVEA toolkit (SEAVEAtk), integrating state-of-the-art VVUQ tools such as EasyVVUQ, FabSim3, and MUSCLE3 for exascale-ready applications. The funding facilitated collaboration among key UK institutions—including UCL, Brunel, and Imperial College—and supported the deployment of SEAVEA across diverse scientific domains such as climate modelling, fusion research, epidemiology, and fluid dynamics. Additionally, the grant enabled community-building activities like hackathons and training events, which played a critical role in shaping SEAVEA’s usability and adaptability. Overall, UKRI’s support was instrumental in transforming SEAVEA from a conceptual initiative into a robust, multidisciplinary platform for scalable and adaptive uncertainty quantification.