  The typical size of computational meshes needed for realistic geometries and
high-speed flow conditions makes Computational Fluid Dynamics (CFD) impractical
for full-mission performance prediction and control. Reduced-Order Models
(ROMs) in low-speed aerodynamics have come a long way in terms of
reconstructing coherent flow patterns, thus enabling aerodynamic performance
prediction. While many examples of ROMs exist for low-speed aerodynamics, there
is no such broad literature for high-speed flows. We propose to use
physics-enhanced neural fields for prediction of the steady, supersonic flow
around a rocket for resolving: the bow shock profile, the boundary layer
gradients and the wake over a range of incidences. This approach can lead to
the construction of a computationally inexpensive, continuous aerothermodynamic
model of a rocket at realistic flight conditions with applications to
aerodynamic design and mission analysis and control. The use of neural fields
allows to quickly and efficiently sweep the Angle of Attack (AoA) in a
continuous manner, as compared to traditional CFD which requires running
simulations for each discrete incidence value.
