AlphaJet: Automated Conceptual Aircraft Synthesis via Disentangled Generative Priors and Topology-Preserving Evolutionary Search

TL;DR

AlphaJet is an automated aircraft design system that synthesizes feasible 3D aircraft configurations from textual specifications using a novel interpretable shape prior, topology-preserving genetic algorithms, and real-time multi-disciplinary scoring.

Contribution

It introduces an anatomically-disentangled VAE for interpretable shape priors, a topology-elitist genetic algorithm, and mount-aware geometric scoring for automated aircraft synthesis.

Findings

Generates feasible 3D aircraft from textual specs in real time.

Uses a shape prior that aligns with anatomical parameters for interpretability.

Employs a topology-preserving genetic algorithm to maintain diverse configurations.

Abstract

Conceptual aircraft design is traditionally an expert-mediated iterative process in which a human designer proposes a configuration, runs low-order physics, inspects the result, and re-proposes. We present AlphaJet, an end-to-end automated synthesis pipeline that closes this loop. From a textual mission specification (mass, range, cruise speed, hard size envelope, engine count, areal density) AlphaJet evolves a feasible 3D aircraft in real time, scored by a transparent multi-disciplinary fitness function covering aerodynamics, structures, weights, stability, packaging, and geometric mount consistency. Three contributions distinguish our approach: (i) an Anatomically-Disentangled Variational Autoencoder (AD-VAE) whose first 25 latent dimensions are supervised to align with named anatomical parameters, providing an interpretable shape prior; (ii) a topology-elitist genetic algorithm that protects the best individual from each of five tail topologies and triggers stagnation restarts, preventing premature collapse to a single configuration; and (iii) mount-aware geometric scoring that computes signed penetration between engines and other structural parts, eliminating the redundant artifacts common in generative aircraft models. The full loop runs interactively on a CPU and streams every generation to a browser viewer, making it a practical real-world automation tool for early-phase design-space exploration.