The on-going work presented in this article explores different technical approaches and
systems for management and analysis of data obtained from large physics simulations, optimising
the respective data-driven workflows across Cloud-Computing (IaaS) and HPC systems. The
work is carried out in the context of the EXA4MIND Horizon Europe project, which produces an
Extreme Data processing platform, bringing together specialised data management systems and
powerful computing infrastructures. We evaluate two typical use cases with physics simulations
carried out on supercomputing systems at LRZ (Garching b.M./DE) and IT4Innovations
(Ostrava/CZ). These use cases come from different areas of physics – they focus on the treatment
of low energy many-body systems of molecules, and of high-energy (relativistic) elementary
particles, respectively. Accordingly, molecular dynamics (MD, low energy) and plasma
simulation methods (FDTD, Particle-in-Cell, high energy) are used. As often in computationally
supported, data-driven science, a large fraction of the work then goes into postprocessing,
visualising and re-assessing the data, often several times in an iterative process. A well-managed,
integrated and efficient “next-generation” methodology to facilitate and manage such a (re-)use
of the valuable data – in particular in the context of parameter studies – with an eye on FAIR
research data management, is one of our final objectives.