Deep operator learning-based surrogate models for aerothermodynamic
analysis of AEDC hypersonic waverider
Deep operator learning-based surrogate models for aerothermodynamic
analysis of AEDC hypersonic waverider
Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that deep neural networks can be used to approximate operators, learning function-to-function mappings. Creating surrogate models to supplement computationally expensive hypersonic aerothermodynamic models in characterizing the …