File size: 2,723 Bytes
429dd83 7399531 429dd83 7399531 41bae07 9197edb 41bae07 9197edb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
datasets:
- BGLab/FlowBench
license: mit
pipeline_tag: time-series-forecasting
library_name: pytorch-lightning
---
# Time-Dependent DeepONet for FlowBench (FPO)
This repository hosts pre-trained **time-dependent DeepONet** checkpoints used in the paper:
> **Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks**
These models are trained on the **FlowBench FPO** data and learn to predict unsteady flow over complex 2D geometries.
---
## Associated Resources
- **Paper (arXiv):** https://arxiv.org/abs/2512.04434
- **Paper page on Hugging Face:** https://huggingface.co/papers/2512.04434
- **Dataset (FlowBench on Hugging Face):** https://huggingface.co/datasets/BGLab/FlowBench
- **Code (model implementation & training scripts):** https://github.com/baskargroup/TimeDependent-DeepONet
- **Interactive demo (Hugging Face Space):** https://huggingface.co/spaces/arabeh/DeepONet-FPO-demo
---
## Checkpoints
All checkpoints are stored under the `checkpoints/` directory:
- `time-dependent-deeponet_1in.ckpt` – model trained with input sequence length `s = 1`
- `time-dependent-deeponet_4in.ckpt` – model trained with input sequence length `s = 4`
- `time-dependent-deeponet_8in.ckpt` – model trained with input sequence length `s = 8`
- `time-dependent-deeponet_16in.ckpt` – model trained with input sequence length `s = 16`
Each checkpoint contains the weights for the time-dependent DeepONet used in the paper. For the exact architecture, data preprocessing, and training details, please refer to the GitHub repository.
You can download any checkpoint using `huggingface_hub`:
```python
from huggingface_hub import hf_hub_download
import torch
from models.geometric_deeponet.geometric_deeponet import GeometricDeepONetTime
REPO_ID = "arabeh/DeepONet-FlowBench-FPO"
filename = "checkpoints/time-dependent-deeponet_4in.ckpt" # choose 1in / 4in / 8in / 16in
# 1) Download checkpoint file locally
ckpt_path = hf_hub_download(REPO_ID, filename)
# 2) Load the Lightning model from checkpoint
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GeometricDeepONetTime.load_from_checkpoint(ckpt_path, map_location=device)
model = model.eval().to(device)
```
For full, reproducible training and evaluation, including data loading and post-processing, please see:
> https://github.com/baskargroup/TimeDependent-DeepONet
---
## Citation
```bibtex
@article{rabeh2025predicting,
title={Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks},
author={Rabeh, Ali and Murugaiyan, Suresh and Krishnamurthy, Adarsh and Ganapathysubramanian, Baskar},
journal={arXiv preprint arXiv:2512.04434},
year={2025}
}
```
|