Approximation and Model Management in Aerodynamic Optimization with Variable-Fidelity Models

By N. Alexandrov, Robert Michael Lewis, C. Gumbert, L. Green, P. A. Newman. 2001

344 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Statistics and Probability

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Citations in the corpus

A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems (2015, positive): “Examples in the literature include the use of simpliﬁed-physics models in design optimization [3, 167], multigrid approaches to optimization of systems governed by diﬀerential equations [124, 151], and mesh coarsening for solving a linear inverse problem [18].”
Recursive Trust-Region Methods for Multiscale Nonlinear Optimization (2008, methodology): “Further motivation is also provided by the computational success of the low/high-fidelity model management techniques of Alexandrov and Lewis [2, 3] and a recent paper by Borzi and Kunisch [9] on multigrid globalization.”
Surrogate-based Analysis and Optimization (2005, background): “The AMMF can be adapted to handle non-linear constraints and successful applications related to variable fidelity models have been reported [90,91].”
Provably Convergent Multifidelity Optimization Algorithm Not Requiring High-Fidelity Derivatives (2012, methodology): “if at the center of the trust region the low-fidelity function value and derivative are scaled or shifted to be equal to the high-fidelity function and gradient [5–7].”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, background): “updates, which define the correction based on the ratio between sampled high-fidelity and low-fidelity outputs [1, 2].”
Recent advances in surrogate-based optimization (2009, methodology): “Of note is the use of the augmented Lagrangian method for constrained optimization in a surrogate-based trust region search [3].”
Improving variable-fidelity modelling by exploring global design space and radial basis function networks for aerofoil design (2015, methodology): “Variable-complexity modelling (VCM) with trust-region management was introduced by Alexandrov et al. (2001; 2000). This approach allowed for good improvement in the solution convergence, but it still has disadvantages related to the Taylor series expansion. Advanced variable-complexity modelling (AV”
Review of multi-fidelity models (2016, methodology): “A simple discrepancy function is desirable in SM techniques, especially for models such as co-Kriging, where it is explicitly considered, enabling the blending of data across fidelity levels (Alexandrov et al., 2001 [5]).”
Optimal Model Management for Multifidelity Monte Carlo Estimation (2016, background): “models to accelerate convergence to an optimal solution [4, 1, 30, 21].”