Metadata-Version: 2.4
Name: poroflow
Version: 0.1.0
Summary: Physics-informed machine learning for flow in porous media
Project-URL: Homepage, https://github.com/rmanasipov/poroflow
Project-URL: Repository, https://github.com/rmanasipov/poroflow
Author: Roman Manasipov
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.11
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: torch>=2.0.0
Description-Content-Type: text/markdown

# poroflow

> **This package is under active development and not yet ready for production use.**

Physics-informed machine learning for simulation of flow in porous media.

## Overview

`poroflow` provides methods for simulating multiphase flow in porous media using physics-informed machine learning approaches, including:

- **Finite Volume Graph Networks (FVGN)** -- GNN-based solvers with built-in conservation guarantees
- **Differentiable numerical fluxes** -- Godunov flux for correct shock handling
- **Progressive rollout training** -- autoregressive stability for long-horizon predictions

The initial focus is on the Buckley-Leverett equation for two-phase immiscible displacement, with plans to extend to 2D problems, capillary pressure effects, and parametric surrogates.

## Installation

```bash
pip install poroflow
```

## Status

This is an early release to reserve the package name. Functional code will be published in upcoming versions.

## License

MIT
