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. The dataset is hosted at this https URL, baseline models and code at this https URL, and the dataset description at this https URL.
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
Authors: Adam D. Cobb, Anirban Roy, Daniel Elenius, F. Michael Heim, Brian Swensonk, Sydney Whittingtonk, James D. Walker, Theodore Bapty, Joseph Hite, Karthik Ramani, Christopher McComb, Susmit Jha
Advances in Neural Information Processing Systems 36 (2023)
http://doi.org/10.5281/zenodo.6525446
Karthik Ramani
Karthik Ramani is the Donald W. Feddersen Professor of School of Mechanical Engineering at Purdue University, with courtesy appointments in Electrical and Computer Engineering and College of Education. He earned his B.Tech from the Indian Institute of Technology, Madras, in 1985, an MS from Ohio State University, in 1987, and a Ph.D. from Stanford University in 1991, all in Mechanical Engineering. He has received many awards from the National Science Foundation (NSF) and other organizations. He has served in the editorial board of Elsevier Journal of Computer-Aided Design (CAD) and the ASME Journal of Mechanical Design (JMD). In 2008 he was a visiting Professor at Stanford University (computer sciences), research fellow at PARC (formerly Xerox PARC). In 2016 summer he was visiting professor Oxford University Institute of Mathematical Sciences. He also serves on the Engineering Advisory sub-committee for SBIR/STTR for the NSF. In 2006 and 2007, he won the Most Cited Journal Paper award from CAD and the Research Excellence award in the College of Engineering at Purdue University. In 2009, he won the Outstanding Commercialization award from Purdue University. He was the co-founder of the world’s first commercial shape-based parts search engine (VizSeek) and more recently co-founded ZeroUI whose product (Ziro) won the Best of Consumer Electronics Show Finalist (CES 2016). His research interests are in the internet-of-things, augmented reality, modular and flexible robotic platforms, and human-machine interactions. His current projects include computer vision for object detection and grasp planning, modular robotic platform design, shape recognition using geometric deep learning, and physical reality simulation platform. His current research emphasis is to develop a Physical-Simulation Platform that will allow one to realistically simulate interactions between workers, robots, and machines in future workplaces such as factories and warehouses.