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---
license: mit
task_categories:
- other
tags:
- physics
- general-relativity
- neural-fields
- scientific-computing
---
# EinFields Datasets
This repository contains examples of datasets used to train [EinFields](https://github.com/AndreiB137/EinFields), a neural representation designed to compress computationally intensive four-dimensional numerical relativity simulations into compact implicit neural network weights. These datasets correspond to the work presented in the paper [Einstein Fields: A Neural Perspective To Computational General Relativity](https://huggingface.co/papers/2507.11589).
EinFields models the *metric*, the core tensor field of general relativity, enabling derivation of physical quantities via automatic differentiation. Unlike conventional neural fields, EinFields are Neural Tensor Fields where dynamics emerge naturally. Key features include continuum modeling of 4D spacetime, mesh-agnosticity, storage efficiency, derivative accuracy, and ease of use.
## Overview
The datasets include three examples used to train EinFields, namely Schwarzschild, Kerr, and Gravitational Wave (GW) metrics, provided in spherical, Kerr-Schild, and Cartesian coordinates.
## Code
The associated code and framework for EinFields is available on GitHub: [https://github.com/AndreiB137/EinFields](https://github.com/AndreiB137/EinFields)
## Usage
These datasets are designed to be used with the EinFields framework. For detailed installation instructions, examples on how to load and utilize these datasets for training, and further information on the framework's capabilities, please refer to the [EinFields GitHub repository](https://github.com/AndreiB137/EinFields).
## Citation
**Paper**: https://arxiv.org/abs/2507.11589
```
@article{
title={EINSTEIN FIELDS: A NEURAL PERSPECTIVE TO COMPUTATIONAL GENERAL RELATIVITY},
author={Cranganore, Bodnar and Berzins},
year={2025},
eprint={2507.11589},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |