An Online Method for Interpolating Linear Parametric Reduced-Order Models
By David Amsallem, Charbel Farhat. SIAM Journal on Scientific Computing, 2011
350 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering, Control and Systems Engineering, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f448efec1bfaf94b02a1f6804a3c7eafd9326c90
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Karen E. Willcox
- Charbel Farhat
- David Amsallem
- Gianluigi Rozza
- Andrea Manzoni
- Toni Lassila
- Alfio Maria Quarteroni
- Peter Benner
In Control and Systems Engineering
- Karen E. Willcox
- Benjamin Peherstorfer
- Andrea Manzoni
- Max D. Gunzburger
- Charbel Farhat
- Peter Benner
- Serkan Gugercin
- David Amsallem
In Statistical and Nonlinear Physics
- Andrea Manzoni
- Gianluigi Rozza
- Karen E. Willcox
- Benjamin Peherstorfer
- Alfio Maria Quarteroni
- Charbel Farhat
- David Amsallem
- Max D. Gunzburger
Citations in the corpus
- Data-driven operator inference for nonintrusive projection-based model reduction (2016, neutral): “,μM via elementwise interpolation [44, 45, 46, 47].”
- Model Order Reduction in Fluid Dynamics: Challenges and Perspectives (2014, methodology): “In [3, 4] the ROMs computed at different parameter points were interpolated to obtain a new ROM that was valid also in the intermediate zone between the original parameter points.”
- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, positive): “The methods proposed in [9, 188] first perform a congruence transformation of the local basis matrices in { V 1 , . . . , V K } (and similarly for { W 1 , . . . , W K } ), so that the reduced systems are expressed in the same generalized coordinate system—this is to provide a consistent reduced…”
- Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, background): “This is computationally expensive, which calls for techniques that approximate the reduced operator Ã(z) without requiring assembly of A(z) first [155, 7, 67].”
- Stabilization of projection‐based reduced‐order models (2012, methodology): “The classical equations describing the dynamics of this system are considered in state-space (first-order) form and reduced using a two-sided Lanczos moment matching method as described in [16].”
- Nonlinear model order reduction based on local reduced‐order bases (2012, background): “(ii) A reduced-order basis (ROB) describing a lower-dimensional subspace often lacks robustness with respect to parameter variations [9–13].”