Introduction to SIG
Electromagnetic fields cannot be seen or touched, but they are ubiquitous in daily life. Electromagnetic fields are mainly generated by two ways: natural and artificial. Natural electromagnetic fields include the earth's magnetic field, sunlight, and electromagnetic waves generated by thermal radiation of all objects, etc. The natural electromagnetic field gave birth to and promoted human civilization: due to the existence of sunlight, human beings can live on the earth with a suitable temperature, and can obtain sufficient food through the photosynthesis of plants; human beings can navigate by using the geomagnetic field, and then ushered in great voyages and the age of globalization. With the development of science and technology, human beings are no longer satisfied with the natural electromagnetic fields, and have begun to actively emit electromagnetic fields into the environment, and fully tap the application potential of electromagnetic fields. For example, in the field of communication, radio waves are used to listen to broadcasts, high-frequency microwaves are used for mobile phone calls, etc.; another example is the use of electromagnetic wave echoes to prove coal storage in geological exploration.
The applications of electromagnetic fields are numerous. In order to make better use of electromagnetic fields, people study the mechanism of electromagnetic fields through experiments, theories, and calculations. In terms of experiments, in 1820, Oersted accidentally discovered that an energized wire deflected a small magnetic needle in a lecture, thus discovering the phenomenon of electric energy generating magnetism. In 1831, Faraday discovered in experiments that a changing magnetic field can generate an electric field, that is, magnetism can also generate electricity. Maxwell summed up the work of predecessors, put forward the hypothesis of displacement current (changing electric field can generate magnetic field), and perfected the theory of electromagnetism. Ultimately, Maxwell expressed the electromagnetic field theory in a concise, symmetrical and perfect mathematical form, namely Maxwell's equations. With the development of computer technology, people use numerical calculation methods to solve Maxwell's equations and simulate the distribution of electromagnetic fields in space. In this way, the cost of experiments can be saved, and electronic devices that better meet the needs can be designed through simulation. Traditional electromagnetic calculation methods include accurate full-wave methods and high-frequency approximation methods. Full-wave methods such as Finite-Difference Time-Domain method (FDTD), finite element (Finite-Element-Method, FEM), method of moments (Method of MoMents, MoM), etc.; high-frequency approximation methods such as geometric Optical method (Geometrical Optics, GO), physical optics (physical optics, PO), etc.
Numerical calculations can better assist the design of electronic products, but there are still many defects in traditional numerical methods, such as the need for complex grid division, iterative calculations, complex calculation processes, and long calculation cycles. The universal approximation and high-efficiency reasoning capabilities of neural networks give neural networks potential advantages in solving differential equations. To this end, Shengsi MindSpore Elec AI Special Interest Group (abbreviation: Intelligent Electromagnetic AI SIG) was formally established, and is recruiting like-minded partners from the open source community.
MindSpore Elec AI SIG Mission
Focusing on various electromagnetic application scenarios in actual production, explore and study AI-based electromagnetic forward and inverse problems under the framework of Shengsi MindSpore. For example, develop an efficient and accurate AI electromagnetic model, build an efficient MindSpore Elec basic toolkit, and improve the design efficiency of electronic products.
MindSpore Elec code repository
- Mindspore Elec repository
- Mindspore Elec SIG repository
SIG group key work direction
MindSpore Elec basic toolkit construction
Build the MindSpore Elec basic toolkit based on MindSpore. The basic toolkit has built-in data construction and conversion, simulation calculation and result visualization, etc.
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Data construction and conversion: support the geometric construction of CSG (Constructive Solid Geometry, CSG) mode, and the efficient tensor conversion of cst and stp data (data formats supported by commercial software such as CST).
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Simulation calculation:
a) AI electromagnetic basic model library: Provides physical and data-driven AI electromagnetic models. Physical driving does not require additional label data, only equations and initial boundary conditions are required; data-driven means that training needs to use data generated by simulation or experiments.
b) Optimization strategy: data compression can effectively reduce the amount of storage and calculation of the neural network; multi-scale filtering and dynamic adaptive weighting can improve the accuracy of the model and overcome problems such as point source singularity; small sample learning is mainly to reduce the amount of training data , saving training costs.
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Result visualization: Simulation results such as S parameters or electromagnetic fields can be saved in CSV and VTK files. MindInsight can display the changes in the loss function during the training process, and display the results on the webpage in the form of pictures; Paraview is a third-party open source software that has advanced functions such as dynamically displaying slices and flips.
AI electromagnetic simulation model and method construction
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Research on end-to-end differentiable traditional electromagnetic simulation methods: build traditional electromagnetic simulation methods such as FDTD/finite element/moment method based on MindSpore, and form an end-to-end differentiable AI fusion method. In this way, MindSpore can be used to accelerate traditional numerical methods to generate data for model training, and it can also implement applications such as data assimilation and electromagnetic inversion based on the automatic differentiation mechanism.
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AI electromagnetic simulation fusion algorithm research: physics-driven (such as PINNs method) and data-driven AI methods, and algorithm innovation for physics and data fusion, etc.
Application of AI electromagnetic simulation model
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Positive problems: Electromagnetic simulation of base station antennas, radar antennas, radio frequency circuits and systems.
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Inverse problems: electromagnetic metamaterial design, radar exploration, electromagnetic imaging, etc.
MindSpore Elec AI SIG work plan
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Early stage: Focusing on members' academic exchange activities, monthly online exchange activities are organized, focusing on the issues involved in AI electromagnetics, introducing the research progress and discussing the difficulties in the research work.
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Later stage: Through cooperative development and other modes, carry out cooperative research on electromagnetic AI issues among domestic universities and enterprises.
Composition of MindSpore Elec AI SIG
lead members:
- Weibing Lu, Dean/Professor, Research Institute of Southeast University
- Wu Yang, Associate Researcher, School of Information Science and Engineering, Southeast University
- Yong Xu, Senior Engineer, Jiangsu Haoyun Liande Information Technology Co., Ltd.
- Jiaqi Li, Associate Professor, School of Physics, Southeast University
- Xinlei Chen, Associate Professor, School of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics
team members:
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Members: Yujie Yuan, Shengsi MindSpore evangelist
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Member: Kyang, Senior Engineer of Huawei MindSpore
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Member: Rui Weng, Ph.D student, School of Information Science and Engineering, Southeast University
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Member: Adrian Lee, Senior Engineer of Huawei MindSpore
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Member: Lulu Zhang, Senior Engineer of Huawei MindSpore
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Member: Jie Qin, Master student, School of Information Science and Engineering, Southeast University
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Member: Zhe Zhang, Master student, School of Information Science and Engineering, Southeast University
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Member: Dingyi Sun, Master student, School of Information Science and Engineering, Southeast University