DrivAerNet is a large-scale, high-fidelity CFD dataset of 3D industry-standard car shapes designed for data-driven aerodynamic design. It comprises 4000 high-quality 3D car meshes and their corresponding aerodynamic performance coefficients, alongside full 3D flow field information. It includes: - CFD Simulation Data: The raw dataset, including full 3D pressure, velocity fields, and wall-shear stresses, computed using 8-16 million mesh elements has a total size of $\sim$ 16TB. - Curated CFD Simulations: For ease of access and use, a streamlined version of the CFD simulation data is provided, refined to include key insights and data, reducing the size to $\sim$ 1TB. - 3D Car Meshes: A total of 4000 designs, showcasing a variety of conventional car shapes and emphasizing the impact of minor geometric modifications on aerodynamic efficiency. The 3D meshes and aerodynamic coefficients $\sim$ 84GB. - 2D slices include the car's wake in the $x$-direction and the symmetry plane in the $y$-direction $\sim$ 12GB.
The DeepNets-1M dataset is composed of neural network architectures represented as graphs where nodes are operations (convolution, pooling, etc.) and edges correspond to the forward pass flow of data through the network. DeepNets-1M has 1 million training architectures and 1402 in-distribution (ID) and out-of-distribution (OOD) evaluation architectures: 500 validation and 500 testing ID architectures, 100 wide OOD architectures, 100 deep OOD architectures, 100 dense OOD architectures, 100 OOD archtectures without batch normalization, and 2 predefined architectures (ResNet-50 and 12 layer Visual Transformer). For 1402 evaluation architectures, DeepNets-1M includes accuracies of the networks on CIFAR-10 and ImageNet after training them with stochastic gradient descent (SGD). Besides accuracy, other properties of evaluation architectures are included: accuracy on noisy images, inference and convergence time. These properties of architectures can enable training neural architecture search models. The DeepNets-1M is used to train and evaluate parameter prediction models such as Graph HyperNetworks. These models can predict all parameters for a given network (graph) in a single forward pass and the results can be compared to optimizing parameters with SGD.
Structured atmospheric data for AI/ML Long-term, pre-processed, atmospheric datasets for use in Machine Learning/AI based forecasting. Initially intended to predict AOD, however can be adapted for prediction of other atmospheric particles. Machine Learning ready NumPy* dataset constructed by pre-processing selected atmospheric variables at 5 pressure levels form ERA5 reanalysis (resulting in 35 features) and AOD data from MODIS on board of Aqua and Terra satellites. This is a long-term daily dataset which spans 20 years from 1st Jan 2003 to 31st Dec 2022 and is homogeneously structured into 1ºx1º grid cells. Missing days and AOD values from MODIS were imputed using Lattice Kriging method (Python code used for imputation included as Jupyter Notebook 'Combine_impute_AOD.ipynb'), but raw (unimputed) MODIS data are also available. All datasets were created for a purpose of training Convolutional Neural Network model designed to forecast Saharan dust (DustNet). These datasets can also be used to train other ML models, or indeed to forecast other variables. This dataset was used to train the DustNet model and predict 24-hr ahead AOD. Please see doi: 10.5281/zenodo.10722953 for further details on predicting AOD and the DustNet model code. *datasets are NumPy arrays (v1.23) created in Python v3.8.18.