A general reduced-order neural operator for spatio-temporal predictive learning on complex spatial domains
This repository contains code accompanying our paper ["A general reduced-order neural operator for spatio-temporal predictive learning on complex spatial domains"]. For questions, feel free to contact us (qlmeng@nuaa.edu.cn).
Dependencies:
- Python (tested on 3.8.11)
- PyTorch (tested on 1.8.0)
Additionally, we need an open-source Python package Lapy (https://github.com/Deep-MI/LaPy/tree/main) for differential geometry on triangle and tetrahedra meshes, which is used to calculate LBO basis. If you fail to install it, try to add the lapy folder included in our source code into your path.
The datasets of Case1-Case6 can be found in here. Please place the dataset in the "data" folder of the corresponding case.
Case1-Burgers.mat
├── Input: U_initial 4000*415*100
└── Output: U_field 4000*415
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Case2-Wave.mat
├── Input: U_field 2000*100*506
└── Output: U_source 2000*100
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Case3-Layout.mat
├── Input: layout 1200*1168
└── Output: T_field 1200*1168*151
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Case4-Qianyuan.mat
├── Input: Tair_time 600*151*6
└── Output: T_field 600*151*2743
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Case5-Qianyuan.mat
├── Input: T_field 600*151*2743
└── Output: D_field 600*2743
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Case6-BloodFlow.mat
├── Input: `BC_time`(500*121*6)
└── Output: `velocity_x`(500*1656*121),`velocity_y`,`velocity_z`
Here we present the code for case1, and the code for other cases is similar. you can run the codes by executing Parallel_main.py to quickly obtain the results. Note that each experiment is repeated five times, the same setup as in our paper. Each case also retains the setting of hyperparameters in the paper.
python Parallel_main.py
