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Electromagnetic simulation refers to simulating the propagation characteristics of electromagnetic waves in objects or space through computation. It is widely used in scenarios such as mobile phone tolerance simulation, antenna optimization, and chip design. Conventional numerical methods, such as finite difference and finite element, require mesh segmentation and iterative computation. The simulation process is complex and the computation time is long, which cannot meet the product design requirements. With the universal approximation theorem and efficient inference capability, the AI method can improve the simulation efficiency.
MindElec is an AI electromagnetic simulation toolkit developed based on MindSpore. It consists of the electromagnetic model library, data build and conversion, simulation computation, and result visualization. End-to-end AI electromagnetic simulation is supported. Currently, Huawei has achieved phase achievements in the tolerance scenario of Huawei mobile phones. Compared with the commercial simulation software, the S parameter error of AI electromagnetic simulation is about 2%, and the end-to-end simulation speed is improved by more than 10 times.
Supports geometric construction in constructive solid geometry (CSG) mode, such as the intersection set, union set, and difference set of rectangles and circles, and also supports efficient tensor conversion of CST and STP data (data formats supported by commercial software such as CST). In the future, we will support smart grid division for traditional scientific computing.
Provides the physical-driven and data-driven AI electromagnetic models. Physical-driven model refers to network training that does not require additional label data. Only equations and initial boundary conditions are required. Data-driven model refers to training that requires data generated through simulation or experiments. Compared with the data-driven model, the physical-driven model has the advantage of avoiding problems such as cost and mesh independence caused by data generation. The disadvantage of the physical-driven model is that the expression form of the equation needs to be specified and technical challenges such as point source singularity, multi-task loss function, and generalization need to be overcome.
Provides a series of optimization strategies to improve physical-driven and data-driven model accuracy and reduce training costs. Data compression can effectively reduce the storage and computation workload of the neural network. Multi-scale filtering and dynamic adaptive weighting can improve the model accuracy and overcome the problems such as point source singularity. Few-shot learning will be completed subsequently to reduce the training data volume and training cost.
The simulation results, such as the S parameters or electromagnetic fields, can be saved in the CSV or VTK files. MindInsight can display the loss function changes during the training process and display the results on the web page in the form of images. ParaView is the third-party open-source software and can dynamically display advanced functions such as slicing and flipping.
Hardware | Operating System | Status |
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Ascend 910 | Ubuntu-x86 | ✔️ |
Ubuntu-aarch64 | ✔️ | |
EulerOS-aarch64 | ✔️ | |
CentOS-x86 | ✔️ | |
CentOS-aarch64 | ✔️ |
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/mindscience/{arch}/mindscience_mindelec_ascend-{version}-{python_version}-linux_{arch}.whl -i https://pypi.tuna.tsinghua.edu.cn/simple
- When the network is connected, dependencies of the MindElec installation package are automatically downloaded during the .whl package installation. For details about dependencies, see setup.py. Pointcloud data generation depends on pythonocc, please install the dependencies by yourself.
{version}
denotes the version of MindElec. For example, when you are installing MindElec 0.1.0,{version}
should be 0.1.0.{arch}
denotes the system architecture. For example, the Linux system you are using is x86 architecture 64-bit,{arch}
should be x86_64. If the system is ARM architecture 64-bit, then it should be aarch64.{python_version}
specifies the python version of which MindElec is built. If you wish to use Python3.7.5,{python_version}
should be cp37-cp37m. If Python3.9.0 is used, it should be cp39-cp39.
Download the source code from the code repository.
cd ~
git clone https://gitee.com/mindspore/mindscience.git
Build and install MindElec.
cd ~/MindElec
bash build.sh
pip install output/mindscience_mindelec_ascend-{version}-cp37-cp37m-linux_{x86_64}.whl -i https://pypi.tuna.tsinghua.edu.cn/simple
For details about MindElec APIs, see the API page.
Run the following command. If the error message No module named 'mindelec'
is not displayed, the installation is successful.
python -c 'import mindelec'
For details about how to quickly use the AI electromagnetic simulation toolkit for training and inference, see MindElec Guide.
For more details about the installation guides, tutorials, and APIs, see MindElec Documents.
Make your contribution. For more details, please refer to our Contributor Wiki
MindScience is scientific computing kits for various industries based on the converged MindSpore framework.
Jupyter Notebook Python Unity3D Asset Pickle nesC other
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