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https://github.com/sciann/sciann
Deep learning for Engineers - Physics Informed Deep Learning
https://github.com/sciann/sciann
Last synced: about 2 months ago
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Deep learning for Engineers - Physics Informed Deep Learning
- Host: GitHub
- URL: https://github.com/sciann/sciann
- Owner: sciann
- License: other
- Created: 2019-07-20T20:13:06.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-20T23:10:12.000Z (9 months ago)
- Last Synced: 2024-07-22T20:19:39.739Z (about 2 months ago)
- Language: Python
- Homepage: htttp://sciann.com
- Size: 515 KB
- Stars: 310
- Watchers: 11
- Forks: 112
- Open Issues: 52
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-scientific-machine-learning - `code`
- awesome-sciml - sciann/sciann: Deep learning for Engineers - Physics Informed Deep Learning
- awesome-machine-learning-fluid-mechanics - SciANN - informed deep learning. (Frameworks)
README
[](https://badge.fury.io/py/SciANN)
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[](https://github.com/sciann/sciann/blob/master/LICENSE)
# :warning: NOTE :warning:
Thanks for your interest in this project. This project is no longer maintained, so please be aware of that. The last tests are based on `python==3.9` and `tensorflow==2.10`. It should be less sensitive to Python updates, but `Keras / TensorFlow` changes rapidly. So I strongly advise to use SciANN on `TensorFlow/Keras == 2.10`.
# SciANN: Neural Networks for Scientific Computations
SciANN is a Keras wrapper for scientific computations and physics-informed deep learning.
## New to SciANN?
SciANN is a high-level artificial neural networks API, written in Python using [Keras](https://keras.io) and [TensorFlow](https://www.tensorflow.org) backends. It is developed with a focus on enabling fast experimentation with different networks architectures and with emphasis on scientific computations, physics informed deep learing, and inversion. *Being able to start deep-learning in a very few lines of code is key to doing good research.*
Use SciANN if you need a deep learning library that:
- Allows for easy and fast prototyping.
- Allows the use of complex deep neural networks.
- Takes advantage TensorFlow and Keras features including seamlessly running on CPU and GPU.
For more details, check out our review paper at [https://arxiv.org/abs/2005.08803](https://arxiv.org/abs/2005.08803) and the documentation at [SciANN.com](https://sciann.com).
Cite SciANN in your publications if it helps your research:
```
@article{haghighat2021sciann,
title={SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks},
author={Haghighat, Ehsan and Juanes, Ruben},
journal={Computer Methods in Applied Mechanics and Engineering},
volume={373},
pages={113552},
year={2021},
publisher={Elsevier}
}
```
SciANN is compatible with: __Python 3.8-3.10__.
Have questions or like collaborations, email [Ehsan Haghighat](mailto:[email protected]).
------------------
## Getting started: 30 seconds to SciANN
The core data structure of SciANN is a `Functional`, a way to organize inputs (`Variables`) and outputs (`Fields`) of a network.
Targets are imposed on `Functional` instances using `Constraint`s.
The SciANN model (`SciModel`) is formed from inputs (`Variables`) and targets(`Constraints`). The model is then trained by calling the `solve` function.
Here is the simplest `SciANN` model:
```python
from sciann import Variable, Functional, SciModel
from sciann.constraints import Data
x = Variable('x')
y = Functional('y')
# y_true is a Numpy array of (N,1) -- with N as number of samples.
model = SciModel(x, Data(y))
```
This is associated to the simplest neural network possible, i.e. a linear relation between the input variable `x` and the output variable `y` with only two parameters to be learned.
Plotting a network is as easy as passing a file_name to the SciModel:
```python
model = SciModel(x, Data(y), plot_to_file='file_path')
```
Once your model looks good, perform the learning with `.solve()`:
```python
# x_true is a Numpy array of (N,1) -- with N as number of samples.
model.train(x_true, y_true, epochs=5, batch_size=32)
```
You can iterate on your training data in batches and in multiple epochs. Please check [Keras](https://keras.io) documentation on `model.fit` for more information on possible options.
You can evaluate the model any time on new data:
```python
classes = model.predict(x_test, batch_size=128)
```
In the [application folder](https://github.com/sciann/sciann-applications) of the repository, you will find some examples of Linear Elasticity, Flow, Flow in Porous Media, etc.
------------------
## Installation
Before installing SciANN, you need to install the TensorFlow and Keras.
- [TensorFlow installation instructions](https://www.tensorflow.org/install/).
- [Keras installation instructions](https://keras.io/#installation).
You may also consider installing the following **optional dependencies**:
- [cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/) (recommended if you plan on running Keras on GPU).
- HDF5 and [h5py](http://docs.h5py.org/en/latest/build.html) (required if you plan on saving Keras/SciANN models to disk).
- [graphviz](https://graphviz.gitlab.io/download/) and [pydot](https://github.com/erocarrera/pydot) (used by [visualization utilities](https://keras.io/visualization/) to plot model graphs).
Then, you can install SciANN itself. There are two ways to install SciANN:
- **Install SciANN from PyPI (recommended):**
Note: These installation steps assume that you are on a Linux or Mac environment.
If you are on Windows, you will need to remove `sudo` to run the commands below.
```sh
sudo pip install sciann
```
If you are using a virtualenv, you may want to avoid using sudo:
```sh
pip install sciann
```
Note: To install tensorflow/sicann on M1 Apple systems, do the following:
```sh
conda install -c apple tensorflow-deps=2.9.0
pip install tensorflow-macos=2.9.0
conda install matpltlib pandas #(ETC)
```
and then
```sh
pip install sciann-macos
```
- **Alternatively: install SciANN from the GitHub source:**
First, clone SciANN using `git`:
```sh
git clone https://github.com/sciann/sciann.git
```
Then, `cd` to the SciANN folder and run the install command:
```sh
sudo python setup.py install
```
or
```sh
sudo pip install .
```
------------------
## Why this name, SciANN?
Scientific Computational with Artificial Neural Networks.
Scientific computations include solving ODEs, PDEs, Integration, Differentiation, Curve Fitting, etc.
------------------