Optimization and Generation in Aerodynamics Inverse Design

• URL: http://arxiv.org/abs/2602.03582v2
• Date: Thu, 05 Feb 2026 15:47:18 GMT
• Title: Optimization and Generation in Aerodynamics Inverse Design
• Authors: Huaguan Chen, Ning Lin, Luxi Chen, Rui Zhang, Wenbing Huang, Chongxuan Li, Hao Sun,
• Abstract summary: Inverse design with physics-based objectives is challenging because it couples high-dimensional geometry with expensive simulations.<n>We revisit inverse design through two canonical solutions, the optimal design point and the optimal design distribution.<n>We propose a new training loss for cost predictors and a density-gradient optimization method that improves objectives while preserving plausible shapes.
• Score: 41.756961128464816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inverse design with physics-based objectives is challenging because it couples high-dimensional geometry with expensive simulations, as exemplified by aerodynamic shape optimization for drag reduction. We revisit inverse design through two canonical solutions, the optimal design point and the optimal design distribution, and relate them to optimization and guided generation. Building on this view, we propose a new training loss for cost predictors and a density-gradient optimization method that improves objectives while preserving plausible shapes. We further unify existing training-free guided generation methods. To address their inability to approximate conditional covariance in high dimensions, we develop a time- and memory-efficient algorithm for approximate covariance estimation. Experiments on a controlled 2D study and high-fidelity 3D aerodynamic benchmarks (car and aircraft), validated by OpenFOAM simulations and miniature wind-tunnel tests with 3D-printed prototypes, demonstrate consistent gains in both optimization and guided generation. Additional offline RL results further support the generality of our approach.

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