Reduced-order modeling - New approaches for computational physics

By Philip S. Beran, W. Silva. 2001

371 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3d582fb02cd5dd088dbddd8bb1b967814bad8681

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In Statistical and Nonlinear Physics

Citations in the corpus

A model-reduction approach in micromechanics of materials preserving the variational structure of constitutive relations (2016, methodology): “Model-reduction has a long history in Fluid Mechanics (see Sirovich, 1987; Holmes et al., 1996, for instance) and in many other fields of computational physics (Lucia et al., 2004).”
Interpolation-based reduced-order modelling for steady transonic flows via manifold learning (2014, methodology): “The POD is either used within a Galerkin projection framework (POD-Galerkin) (Lucia, Beran, and Silva 2004), or it is combined with a CFD flux residual minimisation scheme (LeGresley and Alonso 2001; Zimmermann and Görtz 2012), or it is coupled with an interpolation method (POD+I) (Ly and Tran…”
A physics-informed variational DeepONet for predicting the crack path in brittle materials (2021, background): “This problem has prompted a burgeoning literature on reduced-order approaches [20–22], which use existing data sets to create fast emulators often at the expense of accuracy, stability, and generalization.”
Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics (2014, background): “ROMs can also be classified into time domain and frequency domain approaches [9].”
Reduced-Order Nonlinear Unsteady Aerodynamic Modeling Using a Surrogate-Based Recurrence Framework (2010, background): “Examples of such approaches include proper orthogonal decomposition (POD) [4–7], Volterra series [4,8], and surrogate-based approaches [9–13].”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, neutral): “[252] D.”
Aeroelastic and Aerothermoelastic Analysis in Hypersonic Flow: Past, Present, and Future (2011, methodology): “POD represents a spectral method for ROM, where an orthogonal modal basis is computed from snapshots of the full-order system response to relevant inputs [82].”
Using Automatic Differentiation to Create a Nonlinear Reduced-Order-Model Aerodynamic Solver (2010, neutral): “[3], and Barone and Payne [4]).”