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Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design

arXiv cs.LG / 3/17/2026

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Key Points

  • The paper presents a simulation-based inference approach to design eVTOL aircraft by learning a posterior over the full design space using two diffusion models.
  • It samples over discrete topologies and their corresponding continuous parameters, forming a hierarchical probabilistic model for design Generation.
  • The first diffusion model leverages RDLM and UWMs to sample topologies from a discrete-continuous space, while the second uses a masked diffusion method to sample parameters conditioned on topology.
  • The approach rediscovers known design trends and physical laws in aircraft design while significantly accelerating the design-generation process.

Abstract

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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