semvec/pypi-packages huggingface dataset:
linpde-gp 0.0.1
Linear PDE Solvers as Gaussian Process Inference
License: MIT
Requires Python: <3.11,>=3.10
Keywords: partial-differential-equations,gaussian-processes,probabilistic-numerics,galerkin-method,finite-element-method,collocation-method,spectral-methods

# LinPDE-GP: Linear PDE Solvers based on GP Regression

Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"

## Getting Started

### Cloning the Repository

This repository includes Git submodules, so it is best cloned via

```shell
git clone --recurse-submodules git@github.com:marvinpfoertner/linpde-gp.git
```

If you forgot the `--recurse-submodules` flag when cloning, simply run

```shell
git submodule update --init --recursive
```

inside the repository.

### Installing a Full Development Environment

```shell
cd path/to/linpde-gp
pip install -r dev-requirements.txt
```

## Citation

If you use this software, please cite our paper.

```bibtex
@misc{Pfoertner2022LinPDEGP,
  author = {Pf\"ortner, Marvin and Steinwart, Ingo and Hennig, Philipp and Wenger, Jonathan},
  title = {Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers},
  year = {2022},
  publisher = {arXiv},
  doi = {10.48550/arxiv.2212.12474},
  url = {https://arxiv.org/abs/2212.12474}
}
```