Optimize the shape by descending through the surrogate
TripOptimizer is a fully differentiable deep-learning framework for rapid aerodynamic analysis and shape optimization. A triplane variational autoencoder compresses 3D car geometry into a structured latent space; a drag-prediction head evaluates aerodynamics directly from that latent. Because the whole pipeline is differentiable, drag gradients flow back into the latent space — turning shape optimization into gradient descent.
What the paper delivers
Triplane geometry encoding
Three axis-aligned feature planes represent full 3D car shapes compactly, capturing fine geometric detail without volumetric memory costs.
Drag prediction from latents
Aerodynamic performance is predicted directly in latent space — no meshing, no simulation in the loop.
Gradient-based 3D design
End-to-end differentiability enables generative optimization of industry-standard designs, producing lower-drag variants in minutes rather than CFD-weeks.
Citation
@article{vatani2025tripoptimizer,
title = {TripOptimizer: Generative Three-Dimensional Shape Optimization
and Drag Prediction using Triplane VAE Networks},
author = {Vatani, Parsa and Elrefaie, Mohamed and Nazarpour, Farhad
and Ahmed, Faez},
journal = {Physics of Fluids},
volume = {37},
number = {12},
pages = {127113},
year = {2025}
}