CarHoods10k: An industry-grade data set for representation learning and design optimization in engineering applications
Large-scale, high-quality data sets are central to the development of advanced machine
learning techniques that increase the effectiveness of existing optimization methods or even …
learning techniques that increase the effectiveness of existing optimization methods or even …
Variational autoencoders for 3D data processing
Variational autoencoders (VAEs) play an important role in high-dimensional data generation
based on their ability to fuse the stochastic data representation with the power of recent …
based on their ability to fuse the stochastic data representation with the power of recent …
Drivaernet: A parametric car dataset for data-driven aerodynamic design and graph-based drag prediction
This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-
standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network …
standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network …
Quantifying the generative capabilities of variational autoencoders for 3D car point clouds
During each cycle of automotive development, large amounts of geometric data are
generated as results of design studies and simulation tasks. Discovering hidden knowledge …
generated as results of design studies and simulation tasks. Discovering hidden knowledge …
Point2ffd: Learning shape representations of simulation-ready 3d models for engineering design optimization
Methods for learning on 3D point clouds became ubiquitous due to the popularization of 3D
scanning technology and advances of machine learning techniques. Among these methods …
scanning technology and advances of machine learning techniques. Among these methods …
Exploiting linear interpolation of variational autoencoders for satisfying preferences in evolutionary design optimization
In the early design phase of automotive digital development, one of the key challenges for
the designer is to consider multiple-criteria like aerodynamics and structural efficiency …
the designer is to consider multiple-criteria like aerodynamics and structural efficiency …
Approximating the steady-state temperature of 3d electronic systems with convolutional neural networks
M Stipsitz, H Sanchis-Alepuz - Mathematical and Computational …, 2022 - mdpi.com
Thermal simulations are an important part of the design process in many engineering
disciplines. In simulation-based design approaches, a considerable amount of time is spent …
disciplines. In simulation-based design approaches, a considerable amount of time is spent …
DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
We present DrivAerNet++, the largest and most comprehensive multimodal dataset for
aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with …
aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with …
Road segmentation and environment labeling for autonomous vehicles
In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify
various obstacles present in the environment. The labeling techniques that are currently …
various obstacles present in the environment. The labeling techniques that are currently …
Back to meshes: Optimal simulation-ready mesh prototypes for autoencoder-based 3D car point clouds
Point cloud autoencoders were recently introduced as powerful models for data
compression. They learn a lowdimensional set of variables that are suitable as design …
compression. They learn a lowdimensional set of variables that are suitable as design …