Provably Convergent Multifidelity Optimization Algorithm Not Requiring High-Fidelity Derivatives

By Andrew I. March, Karen E. Willcox. 2012

97 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fc042567a5ad818583162a27273f2842ae9de267

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Leifur Þ. Leifsson
2. Slawomir Koziel
3. Yonatan Afework Tesfahunegn

In Statistics and Probability

1. Leifur Þ. Leifsson
2. Slawomir Koziel
3. Yonatan Afework Tesfahunegn

Citations in the corpus

• Review of multi-fidelity models (2016, unknown): “Fluid mechanics Reference An Em Li PF Eu RANS [68] [179] LF - HF - - [7] [22] [26] [38] [57] [58] - LF HF - [129] [130] [138] [180] - LF - - - HF [27] [46] [60] [94] [104] [121] [122] [141] [151] - LF - HF [35] [43] [175] [204] [205] LF HF [9] [85] [135] [191] - - LF - HF [3] [63] [72] [77] [140] [1”
• Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates (2016, methodology): “Techniques tomodify (also called correct) the low-fidelitymodels include bridge functions [23–25], calibration [21,22], space mapping [26–28], shape-preserving response prediction [29,30], adaptive response correction [31], and adaptive response prediction [32].”