3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization

Yuze Hao, Linchao Zhu, Yi Yang

Keywords: Inverse Design, Aerodynamic Shape Optimization

Abstract:

Inverse design aims to design the input variables of a physical system to optimize
a specified objective function, typically formulated as a search or optimization
problem. However, in 3D domains, the design space grows exponentially, rendering
exhaustive grid-based searches infeasible. Recent advances in deep learning have
accelerated inverse design by providing powerful generative priors and differentiable surrogate models. Nevertheless, current methods tend to approximate the
3D design space using 2D projections or fine-tune existing 3D shapes. These
approaches sacrifice volumetric detail and constrain design exploration, preventing
true 3D design from scratch. In this paper, we propose a 3D Inverse Design (3DID)
framework that directly navigates the 3D design space by coupling a continuous
latent representation with a physics-aware optimization strategy. We first learn a
unified physics–geometry embedding that compactly captures shape and physical
field data in a continuous latent space. Then, we introduce a two-stage strategy to
perform physics-aware optimization. In the first stage, a gradient-guided diffusion
sampler explores the global latent manifold. In the second stage, an objectivedriven, topology-preserving refinement further sculpts each candidate toward the
target objective. This enables 3DID to generate high-fidelity 3D geometries, outperforming existing methods in both solution quality and design versatility.

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 5271