Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils.
Khemraj ShuklaVivek OommenAhmad PeyvanMichael PenwardenLuis BravoAnindya GhoshalRobert M. KirbyGeorge Em KarniadakisPublished in: CoRR (2023)
Keyphrases
- neural network
- case study
- shape model
- optimization problems
- global optimization
- highly accurate
- optimization algorithm
- optimization process
- network architecture
- test bed
- shape prior
- shape features
- computationally efficient
- high accuracy
- shape representation
- shape descriptors
- high quality
- learning algorithm
- deep learning
- biologically inspired
- morphological operators
- shape analysis
- numerical simulations
- combinatorial optimization
- deformable models
- range images
- multi objective
- three dimensional