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. Each 2D map and 3D grid has a set of labels associated to it: 2 cosmological parameters characterizing fundamental properties of the Universe, and 4 astrophysical parameters parametrizing the strength of astrophysical processes such as feedback from supernova and supermassive black-holes. The main task this dataset was designed is to perform a robust inference on the value of the cosmological parameters from each map and grid. The data itself was generated from two completely different set of simulations, and it is not obvious that training one model on one will work when predicting on the other. Since simulations of the real Universe may never be perfect, this dataset provides the data to tackle this problem. Solving this problem will help cosmologists to constrain the value of the cosmological parameters with the highest accuracy and therefore unveil the mysteries of our Universe. CMD can also be used for many other tasks, such as field mapping and super-resolution.
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.
A multi-physics dataset of boiling processes. This repository includes downloads, visualizations, and sample applications. This dataset can be used to train operator networks for phase-change phenomena, act as a ground truth for Physics-Informed Neural Networks, or train computer vision models.
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
This dataset is well-structured for the physics-informed training of Neural operators for irregular domain geometry, which provides the FEM results of solving a darcy problem in a domain geometry shape of a pentagram. The Github of the paper that first use this dataset is: https://github.com/WeihengZ/PI-DCON.