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+# Training recipes for External Aerodynamics
+
+![Results from DoMINO for RTWT SC demo](../../../../docs/img/domino_result_rtwt.jpg)
+
+We have a set of model training recipes that highlight key architectures for external automotive aerodynamics use case. 
+The architectures are:
+- DoMINO is a local, multi-scale, point-cloud based model architecture and the recipe trains a model to take STL
+geometries as input and evaluates flow quantities such as pressure and
+wall shear stress on the surface of the car as well as velocity fields and pressure
+in the volume around it.
+- XAeroNet is a collection of scalable models for large-scale external aerodynamic evaluations. It consists of two models, XAeroNet-S and XAeroNet-V for surface and volume predictions, respectively.
+- FIGConvUNet [1], a novel architecture that can efficiently solve CFD problems for large 3D meshes and arbitrary input and output geometries. FIGConvUNet efficiently combines U-shaped architecture, graph information gathering, and integration, learning efficient latent representation through the representation graph voxel layer.