Active Subspaces for Shape Optimization
By T. Lukaczyk, F. Palacios, Juan José Palacios Alonso, Paul G. Constantine. 2014
173 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/825bfe71548576e55c80215d883425592f96473b
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
- Rohit Tripathy
- Ilias Bilionis
- Zach Grey
- Paul G. Constantine
- Phaedon-Stelios Koutsourelakis
- Constantin Grigo
- Nicholas Zabaras
- Sharmila Karumuri
In Management Science and Operations Research
In Statistics and Probability
- Rohit Tripathy
- Ilias Bilionis
- Zach Grey
- Paul G. Constantine
- Phaedon-Stelios Koutsourelakis
- Constantin Grigo
- Nicholas Zabaras
- Paris Perdikaris
Citations in the corpus
- Active Subspaces of Airfoil Shape Parameterizations (2017, methodology): “[4]) approximate flowfield solutions with parametric representations of airfoil shapes to produce estimates of quantities of interest (QOI).”
- Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018, background): “A recent advancement in dimensionality reduction is active subspaces (AS) [31, 32, 33, 34, 35, 36].”
- Active Subspaces - Emerging Ideas for Dimension Reduction in Parameter Studies (2015, methodology): “The airfoil shape optimization in Chapter 5 consists of joint work with Trent Lukaczyk, Francisco Palacios, and Juan Alonso at Stanford’s Aerospace Design Laboratory [87].”