Research data of variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations

<ul> <li>This is the repository for the paper "<a href="https://www.sciencedirect.com/science/article/pii/S0022509624001807">Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations</a>" (accepted by <a href="https://www.sciencedirect.com/journal/journal-of-the-mechanics-and-physics-of-solids">Journal of the Mechanics and Physics of Solids</a>). Please refer <code>README.md</code> for detailed instructions to reproduce our experiments.</li> <li> <div>This work was supported by National Key Research and Development Program of China (2023YFB3306700 and 2021YFF0306404), and National Natural Science Foundation of China (U2341233 and U21A20429).</div> </li> <li>If you have any technical problems to report or interesting ideas to share, feel free to drop a issue at&nbsp;<a href="https://github.com/BraveDrXuTF/VOL/issues">this issue board.</a> <a href="https://bravedrxutf.github.io/">The first author of this work</a> is now actively seeking full-time internship opportunities and a doctoral position in AI4science, and if you are interested in collaborating with him, kindly consider <a href="mailto:3285935860@qq.com">drop him an email.</a></li> <li>Note: all data and codes in this repository are distributed under&nbsp;<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">CC BY-NC-SA 4.0,</a> and related algorithms are patent-protected. If you want to use materials in this repository for commercial purposes, please contact <a href="mailto:haopeng@dlut.edu.cn">haopeng@dlut.edu.cn</a>.</li> </ul>