  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 response of flow fields at different angles of attack (AoA)
is an ideal application of neural operators. We investigate the use of neural
operators to infer flow fields (volume and surface quantities) around a
geometry based on a 3D waverider model based on experimental data measured at
the Arnold Engineering Development Center (AEDC) Hypervelocity Wind Tunnel
Number 9. We use a DeepONet neural operator which consists of two neural
networks, commonly called a branch and a trunk network. The final output is the
inner product of the output of the branch network and the output of the trunk
net. Because the flow field contains shocks across the entire volume, we
conduct a two-step training approach of the DeepONet that facilitates accurate
approximation of solutions even in the presence of discontinuities. We train
various DeepONet models to understand and predict pressure $(p)$, density
$(\rho)$, velocity $(u)$, heat flux $(Q_w)$, and total shear stress
$(\tau_{w})$ for the AEDC waverider geometry at Ma=7.36 across AoA that range
from $-10^{\circ}$ to $10^{\circ}$ for surface quantities and from
$-14^{\circ}$ to $14^{\circ}$ for volume quantities.
