language:
- en
license: apache-2.0
pipeline_tag: other
tags:
- physics
- PDEs
- surrogate
- FoundationModels
- AI4Sci
Autoencoders as Foundation Models for 3D PDEs with Online Learning
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}
}