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}
}