Multi-fidelity DNNs for function approxiamtion: Details can be found in https://www.sciencedirect.com/science/article/pii/S0021999119307260
multi-fidelity-dnns-pinns's Introduction
multi-fidelity-dnns-pinns's People
multi-fidelity-dnns-pinns's Issues
问题咨询
尊敬的孟老师:
孟老师您好,非常抱歉冒昧打扰到您,我是来自于中南大学土木工程学院的博士生,我看了您在JCP上发表的题为《A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems》的文章非常感兴趣,因为这可以减少算法对数据的依赖,我想把这一方法应用到我们岩土工程的本构模型和内部侵蚀当中,但是内部侵蚀含有一些PDE信息,我也想应用起来,但是我在阅读您的代码过后,我发现您的代码当中没有包含物理信息的建模过程,我注意到您文章中的非饱和土渗流这一案例就对物理信息有所包含,您这一部分的代码可以提供给我学习嘛。
此致!
中南大学兰鹏
Multi-Fidelity-DNNs-PINNs (mf_func.py)
Hi XuhuiM,
The code does not work in TF2. Most of the functions, including the minimizer and sessions are restricted to TF1. If possible, can you upload a version of the code in TF2/Keras.
Thanks and regards,
Sabitoj Singh
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