https://github.com/mvanzulli/tpinn
This repository contains an implementation of Tensorized Physics Informed Neural Networks (TPINNs) for solving physics-based problem
https://github.com/mvanzulli/tpinn
physics-informed-neural-networks quantum-inspired-algorithm tensor-networks tensorflow2
Last synced: 7 days ago
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This repository contains an implementation of Tensorized Physics Informed Neural Networks (TPINNs) for solving physics-based problem
- Host: GitHub
- URL: https://github.com/mvanzulli/tpinn
- Owner: mvanzulli
- Created: 2023-09-01T19:42:41.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-02T23:03:36.000Z (almost 2 years ago)
- Last Synced: 2023-10-29T17:27:57.154Z (over 1 year ago)
- Topics: physics-informed-neural-networks, quantum-inspired-algorithm, tensor-networks, tensorflow2
- Language: Python
- Homepage:
- Size: 203 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Tensorized Deep Physics Informed Neural Networks :atom:
This repository contains an implementation of Tensorized Physics Informed Neural Networks (TPINNs) for solving physics-based problems. TPINNs combine the power of neural networks with the physical laws governing the system to improve accuracy and generalization. :rocket:
## :books: Introduction
Tensorized Physics Informed Neural Networks (TPINNs) are a class of neural networks that incorporate known physical laws into their architecture. By including the governing equations of a system as constraints, TPINNs can solve complex physics-based problems more accurately than traditional neural networks. This repository provides an implementation of TPINNs using TensorFlow. :infinity:
## :heavy_check_mark: Requirements
To run the code in this repository, you need the following dependencies:
- Python (>= 3.6)
- TensorFlow (>= 2.0)
- NumPy (>= 1.18)
## :floppy_disk: Installation
1. Clone the repository:
```
git clone [email protected]:mvanzulli/TPINN.git
```
2. Navigate to the project directory:
```
cd TPINN
```
3. Install the required dependencies:
```
pip install -r requirements.txt
```
## :computer: Usage
To utilize TPINNs in your own projects, follow these steps:
1. Import the necessary modules:
```python
import tensorflow as tf
import numpy as np
from tn_layer import TNLayer
from tn_model import TNModel
```
2. Create an instance of the TNLayer class:
```python
tn_layer = TNLayer(input_dim, bond_dim, activation, kernel_initializer, use_bias, bias_initializer)
```
Replace the arguments with the desired values. `input_dim` is the dimensionality of the input tensor, `bond_dim` is the bond dimension of the TN layer, `activation` is the activation function to use, `kernel_initializer` is the initializer for the weight matrices, `use_bias` specifies whether to include a bias term, and `bias_initializer` is the initializer for the bias term.
3. Create an instance of the TNModel class:
```python
tn_model = TNModel(num_layers, MPO_units, output_dim, bond_dim, activation, use_bias, kernel_initializer, bias_initializer, dif_equation)
```
Replace the arguments with the desired values. `num_layers` is the number of TN layers, `MPO_units` is the number of units in the MPO tensor, `output_dim` is the dimension of the output, `bond_dim` is the bond dimension of the TN layer (optional), `activation` is the activation function to use, `use_bias` specifies whether to include a bias term, `kernel_initializer` is the initializer for the weight matrices, `bias_initializer` is the initializer for the bias term, and `dif_equation` is a callable representing the one-dimensional fourth-order PDE.
4. Use the TNModel to perform forward pass and compute the PDE loss:
```python
y_pred = tn_model.call(x)
pde_loss = tn_model.compute_pde_loss(x)
```
Replace `x` with the input tensor.
## :rocket: Examples
An example usage of TPINNs can be found in the `examples` directory. It demonstrates how to solve a physics-based problem using TPINNs.
## :busts_in_silhouette: Contributing
Contributions to this repository are welcome. Feel free to open issues or submit pull requests.
## :page_with_curl: License
This project is licensed under the [MIT License](LICENSE).