Digiteo - Recherche en sciences et technologies de l'information

Numerical simulation applies to virtually all industries where economic competitiveness is closely linked to the duration of the design/development cycles, to risk management and to the reduction of environmental impact. It can drastically reduce design and maintenance costs, time to market and the impact of regulatory changes. Numerical simulation programs become more complex as they must handle multiple physical phenomena (to take into account the various components of the systems) and multiscale aspects (to manage the components hierarchically). They benefit from ever-increasing computing power, call upon intensive high performance or grid computing and often have to handle very large volumes of data. The schedules of development of software components and hardware components must be consistent. The codes must also meet internationally recognised standards of verification, validation and qualification.

A non-limitative list of topics of interest to Digiteo is:

Numerical methods: model reduction and optimisation, mesh adaptation, domain decomposition, quantification of numerical errors, specific finite element or finite volume
methods (discrete element method, SPH (Smooth Particle Hydrodynamics), discontinuous FEM...). Particularly challenging is the search for generic aspects in methods initially developed for specific applications.

Multiphysics numerical modelling (neutronics, thermodynamics, hydraulics, mechanics, chemistry, metallurgy...). One of the challenges is to interface software components dealing with different physics while maintaining their best performance and without creating defects in the convergence of methods or in the continuity of results.

Multiscale numerical modelling in time (e.g., storage of nuclear waste, from the nanosecond to the century) and in space (e.g., modelling materials from the dislocation level to the structure level): scale space methods, homogenisation/localisation...

Adaptation of software to hardware:
• Use of clusters: load balancing
• Dedicated algorithms for computation on grids/High performance computing (access to TER@TEC, CNRS / Blue gene)

Code supervision:
• Pre-and post-treatment adapted to models with very larges meshes (millions of
degrees of freedom)
• Strategies to couple codes and to achieve multi-scale computations
• Optimisation (cf exploratory topics - optimisation)
• Learning: construction of simplified models, response surface method for non-linear models, genetic algorithms,
• Treatment of uncertainties (modelling and propagation, cf exploratory topics - uncertainty),
• Probabilistic methods (cf exploratory topics - probability in modelling, algorithms, and verification)