Associate team SPADES_EA – Structure-Preserving Approximations of Dynamical systems in Engineering and Science
Principal investigators
Tommaso Taddei, MEMPHIS research team, Inria
Benjamin Sanderse, Scientific Computing group, CWI
Abstract
Model order reduction (MOR) of parametric PDEs is a well-established field in scientific computing that aims to reduce the marginal cost associated with the solution to parametric systems: MOR is motivated by many-query (optimization, parameter sweeps) and real-time (interactive design, monitoring) applications, which naturally arise in the field of continuum mechanics. Despite the numerous examples of applications of MOR to large-scale industrial problems, the practical deployment of MOR techniques remains limited in computational fluid dynamics (CFD). To address the current limitations of MOR methods, several authors have proposed structure-preserving projection techniques and nonlinear data compression methods: the former refer to a class of methods that aim to preserve notable properties (e.g., positivity, entropy conservation) of the solution to the underlying PDE, which are not necessarily guaranteed at the reduced-order level; the latter refer to a class of methods that exploit a nonlinear ansatz to estimate the state field. The objective of the Associate Team SPADES between Inria Team MEMPHIS (PI: Tommaso Taddei) and CWI (PI: Benjamin Sanderse) is to devise effective structure-preserving nonlinear model reduction techniques for unsteady nonlinear PDEs that arise in computational fluid dynamics (CFD). The project benefits from the very complementary expertise in nonlinear approximation methods and structure-preserving reduced-order formulations of the two partners, and has the potential to address the grand challenges of model reduction techniques for a broad range of applications in CFD.
Website: under construction
Keywords: Model order reduction; structure preservation; nonlinear approximations
