Abstract: The Dynamic mode decomposition (DMD) method is an algorithm for searching for an evolution operator (inverse operator problem solutions) in a finite-dimensional problem solution space (numerical or experimentally obtained) in a set of solutions (slices, 'snapshots') in some consecutive moments of time. Expansion of the phase space due to the use of a nonlinear basis (relative to the variables of the problem) allows us to construct a global linear operator describing a linear evolution in the extended 'rectifying space' (the Coopman operator) and the Perron-Frobenius operator that is its adjoint one. The DMD method is equivalent to a compressed representation of a linear evolution operator in the form of a product of rect...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Detection of coherent structures is of crucial importance for understanding the dynamics of a fluid ...
Data-driven schemes are in high demand, given the growing abundance and accessibility to large amoun...
International audienceThe decomposition of experimental data into dynamic modes using a data-based a...
The speed up of finite-volume solvers for compressible flows is a difficult task. There are several ...
International audienceThe dynamic mode decomposition (DMD) is a data-decomposition technique that al...
The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to...
International audienceThe dynamic mode decomposition (DMD) is a data-decomposition technique that al...
International audienceThe decomposition of experimental data into dynamic modes using a data-based a...
Even though fluid flows possess an exceedingly high number of degrees of freedom, their dynamics oft...
Abstract. The speed-up of finite-volume solvers for compressible flows is a difficult task. There ar...
International audienceThe description of coherent features of fluid flow is essential to our underst...
International audienceThe description of coherent features of fluid flow is essential to our underst...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Detection of coherent structures is of crucial importance for understanding the dynamics of a fluid ...
Data-driven schemes are in high demand, given the growing abundance and accessibility to large amoun...
International audienceThe decomposition of experimental data into dynamic modes using a data-based a...
The speed up of finite-volume solvers for compressible flows is a difficult task. There are several ...
International audienceThe dynamic mode decomposition (DMD) is a data-decomposition technique that al...
The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to...
International audienceThe dynamic mode decomposition (DMD) is a data-decomposition technique that al...
International audienceThe decomposition of experimental data into dynamic modes using a data-based a...
Even though fluid flows possess an exceedingly high number of degrees of freedom, their dynamics oft...
Abstract. The speed-up of finite-volume solvers for compressible flows is a difficult task. There ar...
International audienceThe description of coherent features of fluid flow is essential to our underst...
International audienceThe description of coherent features of fluid flow is essential to our underst...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...
Reduced order models exploit underlying low-dimensional patterns of large physical systems, with the...