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README.md

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-license: cc-by-nc-4.0
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+# 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  
+
+---
+
+## 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}
+}
+```