DOI: 10.5281/zenodo.18763653
title: MixeDiT-MaskeDiT: Aircraft Dataset of 30,276 Diverse eVTOL Designs

description: This dataset contains 30,276 eVTOL aircraft, randomly generated and analyzed using SUAVE. The Git repo contains more information.

File structure











/training_data/

   /maskedit/         /data/: training data for MaskeDiT         /model/: trained model for MaskeDiT   /mixedit/         /data/: tokenizing data for MixeDiT         /model/: trained model for MixeDiT






Simulation using SUAVESUAVE is a flexible aerodynamics and flight dynamics simulation tool, which is often used for early-stage conceptual aircraft design. SUAVE ingests an aircraft design and a mission (flight path), and then performs three key coupled analyses sequentially: stability, aerodynamics and propulsion. After the analysis, SUAVE produces a large array of fine-grained aircraft statistics that can vary across the flight path, such as state-of-charge and angle of attack through different phases of flight. In this work, we will focus on a subset of ten summary statistics. As a conceptual design tool, SUAVE relies on traditional mass‑regression models and does not screen out structurally infeasible designs, so we build three modules. (1) A modified mass‑estimation interface that supplies individual component masses to the simulation. (2) A CAD‑based verification tool that detects propeller–wing or propeller–fuselage intersections. (3) A simple structural filter that rejects clearly infeasible configurations (e.g. wingspans over 50m).Design Representation and GenerationWe generate a large design dataset by modeling eVTOLs as trees and using a custom tree‑based probabilistic program. This probabilistic program plays the role of the general prior over all possible eVTOL designs. Starting from the root, each tree branches into propulsion, structural, aerodynamic, energy storage, and avionics subsystems. Each subsystem is further refined into concrete components, such as airframe members and wings, subcomponents, such as propeller rotors, until reaching atomic parameters, such as geometric dimensions and masses. This tree forms a typed, hierarchical JSON schema that functions like a generative grammar. Note that all designs include at least one wing, have one fuselage, and at least one forward propeller.Design Stochasticity and Pre-ProcessingThe design space is constrained by capping the number of components: up to two wings, one horizontal stabilizer, one vertical stabilizer, two clusters of four lifting rotors per wing, two forward propellers per wing, and optionally a single nose-mounted propeller. Any combination of these elements is permitted, resulting in 144 possible configurations. The final design representation for the machine‑learning models is obtained by removing internal fuselage and wing cross‑sections from the maximal design tree and flattening the remainder into a vector. Missing components (e.g., a second wing) are left blank in this vector, with a corresponding mask included in the dataset. We move the generation of internal fuselage and wing cross‑sections to the run‑time simulation, introducing stochasticity when decoding a pre‑processed design. As a result, each design will have different internal fuselage and wing geometries for every SUAVE evaluation, while the key high-level parameters provided to the SBI model, such as total wingspan and fuselage length, remain fixed. DatasetFollowing the outlined data generation pipeline of this section, we initially generate a dataset of 300,000 eVTOL aircraft using our probabilistic program. We then run all designs through our SUAVE evaluation pipeline and filter out designs according structural checks, aircraft stability, and convergence of the SUAVE simulation. This results in a final dataset of 30,276 valid designs.