arabeh/DeepONet-FlowBench-FPO

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:

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

@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}
}