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Deep gaussian process enabled surrogate models for aerodynamic flows
Dushhyanth Rajaram
, Tejas G. Puranik
,
S. Ashwin Renganathan
, Woongje Sung
, Olivia Pinon Fischer
, Dimitri N. Mavris
, Arun Ramamurthy
Aerospace Engineering
Institute for Computational and Data Sciences (ICDS)
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
25
Scopus citations
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Keyphrases
Aerodynamics
100%
Gaussian Process Model
100%
Surrogate Model
100%
Design of Experiments
11%
Time-varying Data
11%
Sensitivity Analysis
11%
Training Time
11%
Training Data
11%
Quantity of Interest
11%
System Response
11%
Test Functions
11%
High Computational Cost
11%
Deep Neural Network
11%
Transonic Airfoil
11%
Scalability Analysis
11%
Training Design
11%
Data Requirements
11%
Predictive Error
11%
Engineering
Gaussians
100%
Surrogate Model
100%
Aerodynamics
100%
Design of Experiments
10%
Airfoil
10%
Computational Cost
10%
Metrics
10%
System Response
10%
Deep Neural Network
10%
Chemical Engineering
Scalability
100%
Deep Neural Network
100%