Reduced models for linear groundwater flow models using empirical orthogonal functions

By P. Vermeulen, A. Heemink, C. te Stroet. 2004

110 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3194dc7a1d3c2645de736334c0aa1565b2a0ddd6

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. Jan Dirk Jansen
2. Danny C. Sorensen
3. Louis J. Durlofsky
4. Saifon Chaturantabut
5. R. Markovinović
6. J. V. Doren
7. Annika Radermacher
8. Stefanie Reese

In Environmental Engineering

1. Louis J. Durlofsky
2. Danny C. Sorensen
3. Jan Dirk Jansen
4. Saifon Chaturantabut
5. R. Markovinović
6. J. V. Doren
7. S. McCoy

In Statistical and Nonlinear Physics

1. Louis J. Durlofsky
2. Saifon Chaturantabut
3. Jan Dirk Jansen
4. Danny C. Sorensen
5. R. Markovinović
6. J. V. Doren
7. Karen E. Willcox
8. Charbel Farhat

Citations in the corpus

• Use of reduced-order models in well control optimization (2017, methodology): “…involving subsurface flow simulation is described in, e.g., Astrid et al. (2011), Cardoso et al. (2009), Gharbi et al. (1997), Gildin et al. (2006), Heijn et al. (2004), Kaleta et al. (2011), Krogstad (2011), Markovinović and Jansen (2006), van Doren et al. (2006) and Vermeulen et al. (2004).”
• Reduced-order optimal control of water flooding using proper orthogonal decomposition (2006, methodology): “Reduced-order modelling and reduced-order control may provide an alternative to this [5,9,10, 19 ].”
• Application of POD and DEIM on dimension reduction of non-linear miscible viscous fingering in porous media (2011, methodology): “In the case of flows described by linear governing equations, for example, [14], the POD–Galerkin technique substantially reduces the computational complexity and simulation time.”
• Nonlinear Model Reduction via Discrete Empirical Interpolation (2010, methodology): “g [90], the POD-Galerkin technique substantially reduces the computational complexity and simulation time.”