---
license: mit
language:
- en
tags:
- physics
- simulation
- Fluid-Dynamics
- Fluid-Solid-Interaction
- multi-physics
pretty_name: Multi-Physics-Dataset
---

# Multi-Physics Fluid-Solid Interaction Dataset

From **Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs**

![Animation](https://github.com/neuraloperator/CoDA-NO/blob/main/fsi_animation_dx.gif?raw=true)

- How to Download ?
  ```python 
  from huggingface_hub import snapshot_download

  folder_path = snapshot_download(
      repo_id="ashiq24/FSI-pde-dataset",
      repo_type="dataset",
      allow_patterns=["fsi-data/*"]
  )
  ```

# Dataset Description: Fluid-Solid Interaction Simulations (fsi-data)

This dataset contains simulations of fluid dynamics (using the Navier-Stokes equations) and elastic wave equations. The primary focus is on simulating the flow of water around an elastic rod.

### Simulation Variables

For each time step (t), the simulation records the following variables:

1. **Velocity (v)**
   - A 2D vector with components:
     - vx: velocity in the x-direction
     - vy: velocity in the y-direction

2. **Pressure (P)**
   - Represents the pressure field at time t

3. **Displacement (d)**
   - A 2D vector representing the displacement of the elastic object and changes in mesh location:
     - dx: displacement in the x-direction
     - dy: displacement in the y-direction

### Mesh Representation

The simulation uses a 2D mesh to represent the spatial domain:

- **Initial Mesh (M0)**
  - Represented as a matrix with N rows and 2 columns
  - Each row contains (x, y) coordinates of a mesh point
  - Example: 
    ```
    x1, y1
    x2, y2
    ...
    xN, yN
    ```

- **Time-dependent Mesh (Mt)**
  - The mesh updates over time based on displacement
  - Calculated as: `Mt = M0 + dt`, where dt is the displacement at time t. That means the displacement at each time step also encodes the shift in the mesh from the initial Mesh `M0`.

# Dataset Description: Computational Fluid Dynamics (cfd-data)

This dataset follows a similar structure to the fsi-data but focuses solely on simulating water movement around a rigid object. Key differences include:

- Only the Navier-Stokes equation is simulated
- The displacement field is zero (no movement of the rigid body)

# Loading Dataset

## FsiDataReader: Fluid-Solid Interaction Data Loader

The `FsiDataReader` class provides an interface to load and process simulation data for fluid-solid interaction. It handles structured datasets containing velocity, pressure, and displacement fields at different time steps.

### **Key Features**
- Loads simulation data for various viscosity (`mu`) values.
- Supports flexible filtering by inlet boundary conditions (`x1` and `x2`).
- Reads and processes both HDF5 and text-based dataset formats.
- Provides a PyTorch `DataLoader` for easy batch processing.

### **Usage Example**
```python
from fsi_data_reader import FsiDataReader

# Initialize the dataset loader
data = FsiDataReader('./fsi-data/', mu=['1.0'], in_lets_x1=['0.0'])

# Access the mesh structure
mesh = data.input_mesh
print(mesh.shape)

# Get a PyTorch DataLoader
data_loader = data.get_loader(batch_size=1, shuffle=False)
```
### **Constructor Parameters**

- **`location (str)`**: Path to the dataset directory.
- **`mu (list)`**: List of viscosity values (`mu`) to load. Must be one of `['0.1', '0.01', '0.5', '5', '1.0', '10.0']`. The value `0.5` should not be mixed with other `mu` values.
- **`in_lets_x1 (list, optional)`**: Inlet boundary condition values for `x1`. Allowed values: `['-4.0', '-2.0', '0.0', '2.0', '4.0', '6.0']`.
- **`in_lets_x2 (list, optional)`**: Inlet boundary condition values for `x2`. Allowed values: `['-4.0', '-2.0', '0.0', '2.0', '4.0', '6.0']`.

---

### **Methods**

#### `get_data(mu, x1, x2)`
Loads simulation data for a specific `mu`, `x1`, and `x2` configuration.

#### `get_data_txt(mu, x1, x2)`
Loads simulation data from text files instead of HDF5 format.

#### `get_loader(batch_size, shuffle=True)`
Returns a PyTorch `DataLoader` with batched velocity, pressure, and displacement data.

### **Visualization**
Code of visulization is provided in `plotting.py` and `data_vis.ipynb`


### **To Cite**
```
@article{rahman2024pretraining,
  title={Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs},
  author={Rahman, Md Ashiqur and George, Robert Joseph and Elleithy, Mogab and Leibovici, Daniel and Li, Zongyi and Bonev, Boris and White, Colin and Berner, Julius and Yeh, Raymond A and Kossaifi, Jean and Azizzadenesheli, Kamyar and Anandkumar, Anima},
  journal={Advances in Neural Information Processing Systems},
  volume={37}
  year={2024}
}
```