Metadata-Version: 2.1
Name: tensordiffeq
Version: 0.2.0
Summary: Distributed PDE Solver in Tensorflow
Home-page: https://github.com/tensordiffeq/tensordiffeq
Author: Levi McClenny
Author-email: levimcclenny@tamu.edu
License: UNKNOWN
Download-URL: https://github.com/tensordiffeq/tensordiffeq/tarball/v0.2.0
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: tensorflow
Requires-Dist: tensorflow-probability
Requires-Dist: pyDOE2
Requires-Dist: pyfiglet
Requires-Dist: tqdm


![TensorDiffEq logo](tdq-banner.png)


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## Efficient and Scalable Physics-Informed Deep Learning

#### Collocation-based PINN PDE solvers for prediction and discovery methods on top of [Tensorflow](https://github.com/tensorflow/tensorflow) 2.X for multi-worker distributed computing. 

Use TensorDiffEq if you require:
- A meshless PINN solver that can distribute over multiple workers (GPUs) for
  forward problems (inference) and inverse problems (discovery)
- Scalable domains - Iterated solver construction allows for N-D spatio-temporal support
  - support for N-D spatial domains with no time element is included
- Self-Adaptive Collocation methods for forward and inverse PINNs
- Intuitive user interface allowing for explicit definitions of variable domains, 
  boundary conditions, initial conditions, and strong-form PDEs 


What makes TensorDiffEq different?
- Completely open-source
- [Self-Adaptive Solvers](https://arxiv.org/abs/2009.04544) for forward and inverse problems, leading to increased accuracy of the solution and stability in training, resulting in 
  less overall training time 
- Multi-GPU distributed training for large or fine-grain spatio-temporal domains
- Built on top of Tensorflow 2.0 for increased support in new functionality exclusive to recent TF releases, such as [XLA support](https://www.tensorflow.org/xla), 
[autograph](https://blog.tensorflow.org/2018/07/autograph-converts-python-into-tensorflow-graphs.html) for efficent graph-building, and [grappler support](https://www.tensorflow.org/guide/graph_optimization)
  for graph optimization* - with no chance of the source code being sunset in a further Tensorflow version release
  
- Intuitive interface - defining domains, BCs, ICs, and strong-form PDEs in "plain english"
  

*In development

If you use TensorDiffEq in your work, please cite it via:

```code
@article{mcclenny2021tensordiffeq,
  title={TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks},
  author={McClenny, Levi D and Haile, Mulugeta A and Braga-Neto, Ulisses M},
  journal={arXiv preprint arXiv:2103.16034},
  year={2021}
}
```

### Thanks to our additional contributors: 
@marcelodallaqua, @ragusa, @emiliocoutinho


