SDGD_PINN

KO | EN

gitlite — search

#javascript #python #react #typescript #go #golang #rust #deep-learning #docker #pytorch #security #openai #ios #open-source

(★ 16)

Tackling the Curse of Dimensionality with Physics-Informed Neural Networks

SDGD_PINN 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# SDGD_PINN
Here is the official PyTorch implementation of the paper: **Tackling the Curse of Dimensionality with Physics-Informed Neural Networks**.

The paper is coauthored by **Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi**.

It has been accepted by **Neural Networks**.

arXiv version: https://arxiv.org/abs/2307.12306

Journal version (Open Access): https://www.sciencedirect.com/science/article/pii/S0893608024002934

# Citations

If you think the code is useful, kindly cite our paper.

```bibtex
@article{hu2024tackling,
  title={Tackling the curse of dimensionality with physics-informed neural networks},
  author={Hu, Zheyuan and Shukla, Khemraj and Karniadakis, George Em and Kawaguchi, Kenji},
  journal={Neural Networks},
  pages={106369},
  year={2024},
  publisher={Elsevier}
}
```

You may also consider citing our other papers on high-dimensional and high-order PINN and PDE, whose codes will also be publicly available once they are accepted.

```bibtex
@article{hu2024hutchinson,
  title={Hutchinson trace estimation for high-dimensional and high-order physics-informed neural networks},
  author={Hu, Zheyuan and Shi, Zekun and Karniadakis, George Em and Kawaguchi, Kenji},
  journal={Computer Methods in Applied Mechanics and Engineering},
  volume={424},
  pages={116883},
  year={2024},
  publisher={Elsevier}
}
```

Code is available at https://github.com/zheyuanhu01/HTE_PINN.

```bibtex
@article{hu2023bias,
  title={Bias-variance trade-off in physics-informed neural networks with randomized smoothing for high-dimensional PDEs},
  author={Hu, Zheyuan and Yang, Zhouhao and Wang, Yezhen and Karniadakis, George Em and Kawaguchi, Kenji},
  journal={arXiv preprint arXiv:2311.15283},
  year={2023}
}
```

```bibtex
@article{hu2024score,
  title={Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations},
  author={Hu, Zheyuan and Zhang, Zhongqiang and Karniadakis, George Em and Kawaguchi, Kenji},
  journal={arXiv preprint arXiv:2402.07465},
  year={2024}
}
```

SDGD_PINN

SDGD_PINN 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

SDGD_PINN

SDGD_PINN 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README