Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS)

The European Center for Advanced Research and Training in Scientific Computing (CERFACS) was founded in 1987 in Toulouse, France, to develop a multidisciplinary expertise in numerical modeling and simulation of a wide range of scientific and engineering challenges. It hosts researchers focused on a variety of topics, including aerodynamics, combustion and propulsion, energy production from traditional and renewable sources, modeling for environment and safety, and climate modeling. The underlying mathematical, algorithmic, and computer science and engineering developments needed to sustain and advance the high-performance computing (HPC) requirements of these applications are pursued and shared between topics. These mutually beneficial interactions are CERFACS’ core value to its shareholders: Airbus Group, Cnes, EDF, Météo France, Onera, Safran, and Total.  

CERFACS is an actor of the international HPC community. It often performs frontier computations through one-time allocations, notably via PRACE and INCITE. It also participates in European projects (via EuroHPC) and Centers of Excellence on HPC, and collaborates with many European HPC centers (CEA, INRIA, JSC, BSC, LRZ). It has a special relationship with French HPC institutes such as CINES, IDRIS and TGCC, coordinated via GENCI. CERFACS’ unique position helps to establish ties between the HPC community and the needs of its industrial partners. It plays a role in training students and engineers in HPC-related topics, in making academic HPC software available and low-friction for engineering applications, as well as exploring the effects of hardware and software evolutions in computing for them. This extends to future technologies and their potential to disrupt scientific computing, such as AI (associated to the ANITI institute) and Quantum computing. 

CERFACS is involved in RAISE mostly through the Compute-Driven Use-Cases towards Exascale (WP3), where it has two focuses. The first is to explore how computational physical models can be reformulated in a hybrid approach by incorporating data-driven elements, keeping important physical constraints in check, and produce the corresponding datasets and metrics for learning procedures applied by collaborators such as Atos-Bull and UOI. The second is to assist the SAFRAN HE hydrogen-burning helicopter engine use-case by offering accurate physical models, as well as hybrid solvers for enhanced accuracy and efficiency in the design. It will also be involved in the Outreach and Services (WP6) work package.