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相关概念视频

Magnetic Fields01:27

Magnetic Fields

7.1K
A moving charge or a current creates a magnetic field in the surrounding space, in addition to its electric field. The magnetic field exerts a force on any other moving charge or current that is present in the field. Like an electric field, the magnetic field is also a vector field. At any position, the direction of the magnetic field is defined as the direction in which the north pole of a compass needle points.
A magnetic field is defined by the force that a charged particle experiences...
7.1K
Electromagnetic Fields01:30

Electromagnetic Fields

2.7K
Electric fields generated by static charges, often referred to as electrostatic fields, are characteristically different from electric fields created by time-varying magnetic fields. While the former is a conservative field, implying that no net work is done on a test charge if it goes around in a complete loop in the field, the latter is, by definition, not a conservative field; net work is done, and it is proportional to the rate of change of magnetic flux.
However, the observation of...
2.7K
Plane Electromagnetic Waves I01:30

Plane Electromagnetic Waves I

4.8K
The existence of combined electric and magnetic fields that propagate through space as electromagnetic (EM) waves is the most significant prediction of Maxwell's equations. As Maxwell's equations hold in free space, the predicted electromagnetic waves do not require a medium for their propagation. An EM wave comprises an electric field, defined as the force per charge on a stationary charge, and a magnetic field, which is the force per charge on a moving charge.
The EM field is assumed to be a...
4.8K
Magnetic Field due to Moving Charges01:23

Magnetic Field due to Moving Charges

11.4K
A stationary charge creates and interacts with the electric field, while a moving charge creates a magnetic field.
Consider a point charge moving with a constant velocity. Like the electric field, the magnetic field at any point is directly proportional to the magnitude of the charge and inversely proportional to the square of the distance between the source point and the field point. However, unlike the electric field, the magnetic field is always perpendicular to the plane containing the line...
11.4K
Induced Electric Fields: Applications01:27

Induced Electric Fields: Applications

2.5K
An important distinction exists between the electric field induced by a changing magnetic field and the electrostatic field produced by a fixed charge distribution. Specifically, the induced electric field is nonconservative because it does not work in moving a charge over a closed path. In contrast, the electrostatic field is conservative and does no net work over a closed path. Hence, electric potential can be associated with the electrostatic field but not the induced field. The following...
2.5K
Plane Electromagnetic Waves II01:29

Plane Electromagnetic Waves II

4.0K
Consider a plane wavefront traveling in position x-direction with a constant speed. This wavefront can be utilized to obtain the relationship between electric and magnetic fields with the help of Faraday's law.
4.0K

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相关实验视频

Updated: Jan 7, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
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快速电磁场模拟使用基于电流密度的物理信息的神经网络.

Zhiwei Gao1,2,3, Cheng-An Sun4,5,6, Zibin Ma6

  • 1School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, China. gao_zhiwei@163.com.

Scientific reports
|December 31, 2025
PubMed
概括

这项研究引入了一个基于物理的神经网络 (PINN) 用于电磁场模拟,比传统方法提高效率和适应性. 该PINN模型准确地解决了Poisson问题.

关键词:
电流密度 电流密度 电流密度深度学习是一种深度学习.有限差分方法的方法.基于物理学的神经网络 (PINN)波桑的方程是什么?

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External Excitation of Neurons Using Electric and Magnetic Fields in One- and Two-dimensional Cultures
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Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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科学领域:

  • 计算电磁学 计算机电磁学
  • 应用物理 应用物理
  • 机器学习在物理学中的应用

背景情况:

  • 传统的电磁场模拟和电流密度问题的数值方法面临效率和适应性方面的挑战.
  • 现有的Poisson方程解答器可能是计算密集的,并且缺乏复杂场景的灵活性.

研究的目的:

  • 引入一种新的物理信息神经网络 (PINN) 模型,用于增强电磁场模拟和电流密度分析.
  • 通过利用与物理原理集成的深度学习来解决传统方法的局限性.
  • 提高解决波桑方程的计算效率和灵活性.

主要方法:

  • 开发一个包含先验物理和数学知识的PINN模型.
  • 将PINN模型应用于两个不同的场景:来自激光目标相互作用的电磁脉冲模拟和用于场电路合的电场计算.
  • 在速度,准确性和适应性方面评估模型的性能.

主要成果:

  • 与传统的解决方案相比,基于PINN的方法证明了计算速度的显著加速.
  • 该模型实现了高精度,相对误差低于1.4%.
  • 观察到对电流密度的变化有更好的适应性.

结论:

  • 开发的PINN模型为电磁场模拟和电流密度挑战提供了强大而高效的工具.
  • 这项研究强调了PINNs在电磁场模拟和潜在预测中的广泛应用.
  • 这项研究验证了将深度学习与物理定律集成到复杂计算问题的有效性.