The group focuses on methodologies combining

• high-fidelity simulations: high-order approximations (in particular HDG formulations) with exact geometrical descriptions (NEFEM),
• reduced order models (viz. PGD) for generalized parametric solutions,
• data assimilation and data-driven models,
• uncertainty quantification and model updating for reliable simulations with goal-oriented error assessment and adaptivity.

These methodologies aim at solving challenging problems in different applications fields:

• Engineering design: flow and wave problems, drag and lift automotive optimization, aeroacoustics, …
• Manufacturing processes: real-time simulation with data assimilation.
• Architectured materials: parametric micro structures for macro properties.
• Patient-specific modeling and simulation

Generalized parametric solutions in Stokes flow
Díez, P., Zlotnik, S. and Huerta, A.
Computer Methods in Applied Mechanics and Engineering , Vol. 326, pp. 223-240, 2017

A semi-analytical scheme for highly oscillatory integrals over tetrahedra
Hospital-Bravo, R.; Sarrate, J. and Díez, P.
International Journal for Numerical Methods in Engineering , Vol. 111, Issue 8, pp. 703-723, 2017

Monitoring a PGD solver for parametric power flow problems with goal-oriented error assessment
García-Blanco, R.; Borzacchillelo, D.; Chinesta, F. and Díez, P.
International Journal for Numerical Methods in Engineering , Vol. 111, Issue 6, pp. 529-552, 2017

Enhanced goal-oriented error assessment and computational strategies in adaptive reduced basis solver for stochastic problems
Serafin, K.; Magnain, B.; Florentin, E.; Parés, N. and Díez, P.
International Journal for Numerical Methods in Engineering , Vol. 110, Issue 5, pp. 440-466, 2017