Optimization and Generation in Aerodynamics Inverse Design
This work addresses aerodynamic inverse design problems for engineers, presenting incremental improvements to existing methods.
The paper tackles the challenge of aerodynamic shape optimization for drag reduction by proposing a new training loss for cost predictors and a density-gradient optimization method that improves objectives while preserving plausible shapes. Experiments on 2D and 3D aerodynamic benchmarks, validated by simulations and wind-tunnel tests, demonstrate consistent gains in both optimization and guided generation.
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.