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

• URL: http://arxiv.org/abs/2306.05562v1
• Date: Thu, 8 Jun 2023 21:07:15 GMT
• Title: AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
• Authors: Adam D. Cobb, Anirban Roy, Daniel Elenius, F. Michael Heim, Brian Swenson, Sydney Whittington, James D. Walker, Theodore Bapty, Joseph Hite, Karthik Ramani, Christopher McComb, Susmit Jha
• Abstract summary: AircraftVerse contains 27,714 diverse air vehicle designs. Each design comprises the following artifacts: a symbolic design tree describing topology propulsion subsystem, battery subsystem, and design details. We present baseline surrogate models that use different modalities of design representation to predict design performance metrics.
• Score: 15.169540193173923
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 https://zenodo.org/record/6525446, baseline models and code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description at https://aircraftverse.onrender.com/.

Related papers

• Exploring the design space of deep-learning-based weather forecasting systems [56.129148006412855]
  This paper systematically analyzes the impact of different design choices on deep-learning-based weather forecasting systems. We study fixed-grid architectures such as UNet, fully convolutional architectures, and transformer-based models. We propose a hybrid system that combines the strong performance of fixed-grid models with the flexibility of grid-invariant architectures.
  arXiv  Detail & Related papers  (2024-10-09T22:25:50Z)
• VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling [3.746111274696241]
  This work explores the use of 3D generative models to explore the design space in the context of vehicle development. We generate diverse 3D models of cars that meet a given set of geometric specifications. We also obtain quick estimates of performance parameters such as aerodynamic drag.
  arXiv  Detail & Related papers  (2024-10-09T16:59:24Z)
• DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks [25.00264553520033]
  DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and estateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification.
  arXiv  Detail & Related papers  (2024-06-13T23:19:48Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings [4.868319717279586]
  We propose a new two-dimensional (2D) representation of 3D shapes to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 9,070 high-quality 3D car meshes labeled by drag coefficients. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an $R2$ value above 0.84 for various car categories.
  arXiv  Detail & Related papers  (2023-05-26T09:33:12Z)
• Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning [4.091593765662773]
  This paper presents a large dataset of thirty thousand ship hulls, each with design and functional performance information. The paper introduces a set of twelve ship hulls from publicly available CAD repositories to showcase the proposed parameterizations ability to accurately reconstruct existing hulls.
  arXiv  Detail & Related papers  (2023-05-14T23:47:20Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Design of Unmanned Air Vehicles Using Transformer Surrogate Models [8.914156789222266]
  We develop an AI Designer that synthesizes novel unmanned aerial vehicles (UAVs) designs. Our approach uses a deep transformer model with a novel domain-specific encoding such that we can evaluate the performance of new proposed designs without running expensive flight dynamics models and CAD tools.
  arXiv  Detail & Related papers  (2022-11-11T21:22:21Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots [55.056709056795206]
  Building domain-specific architectures for autonomous aerial robots is challenging due to a lack of systematic methodology for designing onboard compute. We introduce a novel performance model called the F-1 roofline to help architects understand how to build a balanced computing system. To navigate the cyber-physical design space automatically, we subsequently introduce AutoPilot.
  arXiv  Detail & Related papers  (2021-02-05T03:50:54Z)
• Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency [114.02182755620784]
  We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our framework is shown to outperform the state-of-the-art depth and motion estimation methods.
  arXiv  Detail & Related papers  (2021-02-04T14:26:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.