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Updated README
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arpastrana committed May 9, 2024
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[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/jax-fdm.svg)](https://pypi.python.org/project/jax-fdm)
<!-- [![GitHub - License](https://img.shields.io/github/license/arpastrana/jax_fdm.svg)](https://github.com/arpastrana/jax_fdm) -->

A differentiable, hardware-accelerated framework for inverse form-finding in structural design.
A differentiable, hardware-accelerated framework for constrained form-finding in structural design.

> Crafted with care in the [Form-Finding Lab](http://formfindinglab.princeton.edu/) at [Princeton University](https://princeton.edu) ❤️🇺🇸
![](images/jax_logo.gif)

JAX FDM enables the solution of inverse form-finding problems for discrete force networks using the force density method (FDM) and gradient-based optimization.
JAX FDM enables the solution of inverse problems for the form-finding of discrete force networks using the force density method (FDM) and gradient-based optimization.
It streamlines the integration of form-finding simulations into deep learning models for machine learning research.

## Key features
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Next, install COMPAS and COMPAS VIEW2 via `conda`:

```bash
conda install -c conda-forge compas compas_view2==0.7.0
conda install -c conda-forge compas==1.17.10 compas_view2==0.7.0
```

Finally, install JAX FDM with a one-liner via `pip`:
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