用扩散模型加速电动垂直起降飞机设计，同时生成结构与参数。
Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design
- 分层扩散模型：先生成飞机结构，再条件生成参数。
- 生成速度提升，且符合已知物理规律。
- 适合航空航天设计与生成式AI交叉研究者。
本文生成电动垂直起降（eVTOL）飞机的概念工程设计。采用仿真推断（SBI）范式，学习整个eVTOL设计空间的后验分布。通过在离散飞机拓扑和连续参数空间中采样，提出一种双扩散模型的层次化概率模型。首个模型基于黎曼扩散语言建模（RDLM）与统一世界模型（UWMs），实现对离散与连续空间的拓扑采样；第二个模型引入掩码扩散方法，根据已生成拓扑条件化采样对应参数。该方法重新发现飞机设计中的已知趋势与物理规律，显著加速设计生成过程。
原文摘要 · Abstract (English)
In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm of simulation-based inference (SBI), whereby we look to learn a posterior distribution over the full eVTOL design space. To learn this distribution, we sample over discrete aircraft configurations (topologies) and their corresponding set of continuous parameters. Therefore, we introduce a hierarchical probabilistic model consisting of two diffusion models. The first model leverages recent work on Riemannian Diffusion Language Modeling (RDLM) and Unified World Models (UWMs) to enable us to sample topologies from a discrete and continuous space. For the second model we introduce a masked diffusion approach to sample the corresponding parameters conditioned on the topology. Our approach rediscovers known trends and governing physical laws in aircraft design, while significantly accelerating design generation.
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