Calibrated reduced-order POD-Galerkin system for fluid flow modelling

By M. Couplet, C. Basdevant, Pierre Sagaut. 2005

282 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b2e25dc539ac040d3991ca9a41080a6664c1d9f6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Fangxin Fang
2. Christopher C. Pain
3. Jeff Borggaard
4. Dominique Pelletier
5. Alexander Hay
6. Ionel M. Navon
7. Julien Favier
8. Zoran Popovic

In Statistical and Nonlinear Physics

1. Jeff Borggaard
2. Dominique Pelletier
3. Alexander Hay
4. Ionel M. Navon
5. Julien Favier
6. Zoran Popovic
7. Andrew Lewis
8. Christopher C. Pain

Citations in the corpus

• A POD reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows (2009, methodology): “One way to achieve stabilisation improvements is to add calibrated terms into the POD reduced order equations [19, 20, 21].”
• Model reduction for real-time fluids (2006, methodology): “To increase accuracy, these models can be subsequently calibrated to the observed data [Couplet et al. 2005].”
• On closures for reduced order models—A spectrum of first-principle to machine-learned avenues (2021, unknown): “345 An extension to calibrate all the polynomial coefficients (linear and quadratic) was employed by Couplet et al. , 346 where the cost function is defined to penalize the deviation of the calibrated ROM behavior with respect to the projection of true snapshot data.”
• Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition (2009, methodology): “These results are consistent with the topology of the vortex street (see e.g. Deane et al. 1991; Sirisup & Karniadakis 2004; Couplet et al. 2005; Hay, Borggaard & Pelletier 2008).”
• To appear in the ACM SIGGRAPH conference proceedings Modular Bases for Fluid Dynamics (unknown): “…where the technique is known as proper orthogonal decomposition (POD), Karhunen-Loéve decomposition , or subspace integration [Holmes et al. 1996; Rowley et al. 2006; Ausseur et al. 2004; Sirovich 1987; Couplet et al. 2005; Marion and Temam 1989; Sirisup and Karniadakis 2004; Lumley 1970].”
• Calibration of POD reduced‐order models using Tikhonov regularization (2009, methodology): “State calibration method From a mathematical point of view, the minimization problem based on I(1) is not well posed: several solutions may coexist and convergence is not even guaranteed (see [3] for some arguments).”