Proper orthogonal decomposition extensions for parametric applications in compressible aerodynamics

By Tan Bui-Thanh, M. Damodaran, Karen E. Willcox. 2003

265 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/080d97bfe4555076113a55c281ad270312baeb44

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Citations in the corpus

Interpolation-based reduced-order modelling for steady transonic flows via manifold learning (2014, methodology): “For computing flow predictions, the POD was combined with an interpolation scheme as in Bui-Thanh, Damodaran, and Willcox (2003), in the following referred to as POD+I.”
Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data (2020, methodology): “For instance, Gappy POD has been applied for steady and unsteady flow-field reconstruction in various applications [7–12].”
Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations (2011, methodology): “It has also been used for steady [41, 42] and unsteady [43, 44] ﬂow ﬁeld reconstruction, inverse design [42], design variable mapping for multi-ﬁdelity optimization [45], and to some extent, for non-linear computations [26, 46].”
Data-driven operator inference for nonintrusive projection-based model reduction (2016, background): “The approaches in [23, 24, 25, 26, 27, 28, 29, 30] construct a reduced space and learn a surrogate model that maps inputs to coeﬃcients of the representations of full-model states in the reduced space.”
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, unknown): “Interpolation can be achieved using polynomial or spline interpolation [60, 163, 69], least squares fitting [59, 178], or radial basis function models [20].”