AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs


We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license.

In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023 (Benchmarks and Datasets)
Adam Cobb
Adam Cobb
Senior Computer Scientist

My research interests include Bayesian inference, Bayesian deep learning, Gaussian processes, and quantum machine learning.

Susmit Jha
Susmit Jha
Technical Director, NuSCI

My research interests include artificial intelligence, formal methods, machine learning and dynamical systems.