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Mesh Analysis

Mesh Analysis

Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...

Mesh Analysis with Current Sources

Mesh Analysis with Current Sources

Mesh analysis becomes simpler when analyzing circuits with current sources, whether independent or dependent. The presence of current sources reduces the number of equations required for analysis. Two cases illustrate this:
Current Source in One Mesh: The analysis process is straightforward when a current source is found in only one mesh within the circuit. Mesh currents are assigned as usual, with the mesh containing the current source excluded from the analysis. Kirchhoff's voltage law (KVL)...

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Related Experiment Video

Updated: Jul 15, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Large-scale data-driven inverse EM design based on metacircuit-embedded surface.

Ming Xu¹, Song Zha², Mingtuan Lin³

¹College of Electronic Science and Technology, National University of Defense Technology, Changsha, Hunan, China.

Nature Communications

|July 13, 2026

Summary

A novel metacircuit-embedded surface (MCES) framework enables efficient generation of electromagnetic training data. This advances general-purpose electromagnetic modeling for intelligent applications.

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Last Updated: Jul 15, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Published on: April 15, 2015

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Published on: June 21, 2022

Area of Science:

Electromagnetics
Artificial Intelligence
Materials Science

Background:

General-purpose electromagnetic models are crucial for intelligent applications but hindered by limited training data.
High computational costs of full-wave simulations and specialized topologies restrict data generation.

Purpose of the Study:

To introduce a new framework for efficient generation of large-scale electromagnetic training data.
To develop an AI-driven design method for versatile metasurface applications.

Main Methods:

Developed a metacircuit-embedded surface (MCES) framework with fixed topology and flexible lumped-component metacircuits.
Shifted design focus from full-wave simulation to circuit-level modeling for rapid data generation (over six million samples).
Implemented an MCES-based forward and inverse AI design method.

Main Results:

Achieved efficient generation of over six million training samples using laptop resources.
Demonstrated rich design capabilities across frequency, amplitude, phase, and polarization.
Fabricated three classical metasurfaces exhibiting superior performance over existing advanced designs.

Conclusions:

The MCES framework significantly enhances design efficiency and training data scale for electromagnetic models.
This approach paves the way for the development of future general-purpose electromagnetic models.
Facilitates advanced metasurface design and intelligent electromagnetic applications.