Tadpole

<p align="center"> <a href="https://github.com/tum-pbs/Tadpole"> <img src="https://huggingface.co/thuerey-group/Tadpole/resolve/main/assets/images/tadpole_colorful.png" width="100"/> </a> </p> <h4 align="center">Autoencoders as Foundation Models for 3D PDEs with Online Learning</h4> <p align="center"> <a href="https://arxiv.org/abs/2605.15284"> <img src="https://img.shields.io/badge/arXiv-2605.15284-b31b1b?logo=arxiv" alt="Read on arXiv"/> </a> <a href="https://github.com/tum-pbs/Tadpole"> <img src="https://img.shields.io/badge/Github-Tadpole-181717?logo=github" alt="Github project"/> </a> </p>

About

This repository contains pre-trained weights of Tadpole, a foundation model for three-dimensional partial differential equations (PDEs) introduced in the paper Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning.

Tadpole distinguishes itself from existing PDE foundation models in three key aspects:

(1) Autoencoder Pre-training: Tadpole is pre-trained to learn the inherent representation of PDE solutions, which is more generalizable than the traditional paradigm of training models directly on temporal dynamics.

(2) Online Learning: It utilizes a GPU-based solver to generate diverse data on-the-fly, avoiding storage and I/O bottlenecks associated with massive 3D PDE datasets.

(3) Multi-functionality: Tadpole can be applied to multiple downstream tasks, including autoencoding, dynamics prediction, and generative modeling.

Installation and Loading Pre-trained Weights

Tadpole can be installed via pip:

pip install git+https://github.com/tum-pbs/Tadpole

The pre-trained weights are named as tadpole_{SIZE}_{TYPE}.safetensors, where {SIZE} can be S, B, or L indicating the model size, and {TYPE} can be encoder or decoder. Weights can be loaded through weight_{TYPE} arguments in Tadpole model classes:

from huggingface_hub import hf_hub_download

# Example: loading the B-size model
ae = TadpoleAutoencoder(
    size="B",
    weight_encoder=hf_hub_download(repo_id="thuerey-group/Tadpole", filename="tadpole_b_encoder.safetensors"),
    # or you can also download the weights from Hugging Face and load it locally
    weight_decoder=hf_hub_download(repo_id="thuerey-group/Tadpole", filename="tadpole_b_decoder.safetensors"),
    )

Please refer to the GitHub repository for more details and tutorials.

Note: Currently, pre-trained weights for the B-size model are provided; S- and L-size models will be released in the future.

Citation

@article{Liu2026Tadpole,
  author = {Qiang Liu, Felix Koehler, Benjamin Holzschuh, and Nils Thuerey},
  title = {Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning},
  eprint={2605.15284},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2605.15284} 
}