The ABC Dataset is a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms.
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PasticineLab is a differentiable physics benchmark, which includes a diverse collection of soft body manipulation tasks. In each task, the agent uses manipulators to deform the plasticine into the desired configuration. The underlying physics engine supports differentiable elastic and plastic deformation using the DiffTaichi system, posing many under-explored challenges to robotic agents.
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CMD is a publicly available collection of hundreds of thousands 2D maps and 3D grids containing different properties of the gas, dark matter, and stars from more than 2,000 different universes. The data has been generated from thousands of state-of-the-art (magneto-)hydrodynamic and gravity-only N-body simulations from the CAMELS project.
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Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To fill this gap, we build a large dataset, ClimART, with more than \emph{10 million samples from present, pre-industrial, and future climate conditions}, based on the Canadian Earth System Model. ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed.
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Dataset of low fidelity resolutions of the RANS equations over airfoils.
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This dataset contains data of 125 1-hour simulations of ship motion during various sea states performing random maneuvers in 4 degrees of freedom (surge-sway-yaw-roll). The original ship is a patrol ship developed by Perez et al. 1. We have extended it with a set of two symmetrically placed rudder propellers. Additionally, we simulate wind forces according to Isherwood's wind model 2. Wind-induced waves are generated with the JONSWAP spectrum 3 and the corresponding wave forces are then computed using wave force response amplitude operators (ROA).
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.
Neural network model files and Madgraph event generator outputs used as inputs to the results presented in the paper "Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences" arXiv:2108.11481; 2022 Mach. Learn.: Sci. Technol. 3 015021 Code and model files can be found at: https://github.com/darrendavidprice/science-discovery/tree/master/expressive_gaussian_mixture_models
Workshop Tools Dataset Motivated by the need for a dataset that also includes inertial information about the objects, we contribute the following dataset. It contains 20 common workshop tools, and for each object: - a watertight triangular surface mesh; - a synthetic colored surface point-cloud; - ground truth inertial parameters; - ground truth part-level segmentation; and - a grasping reference frame.