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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Classification of Systems-I01:26

Classification of Systems-I

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Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Related Experiment Video

Updated: Jun 10, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

MAS4SysML: A Multi-Agent Framework for SysML v2 Model Generation from Natural Language.

Yuhao Liu1, Junjie Hou2, Haolong Zhang2

  • 1China Aerospace Academy of Systems Science and Engineering; 13930491501@139.com.

Journal of Visualized Experiments : Jove
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces MAS4SysML, a multi-agent framework for generating SysML v2 code from natural language. It enhances syntactic correctness and semantic consistency in model-based systems engineering (MBSE).

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Systems Engineering
  • Artificial Intelligence

Background:

  • Model-Based Systems Engineering (MBSE) adoption is hindered by challenges in automatically generating formal models from natural language requirements.
  • Large Language Models (LLMs) struggle with the strict syntactic constraints of formal modeling languages and ensuring semantic alignment.

Purpose of the Study:

  • To present MAS4SysML, a multi-agent collaborative framework designed to improve SysML v2 code generation.
  • To enhance syntactic correctness and semantic consistency of generated SysML models within a constrained repair budget.

Main Methods:

  • The MAS4SysML framework decomposes modeling tasks into hierarchical subtasks, formalized as structured task cards.
  • Model code is generated bottom-up, with iterative repair and revalidation guided by diagnostic feedback from a validation environment.
  • Semantic consistency is verified against task cards, and repairs are performed within a predefined budget.

Main Results:

  • MAS4SysML significantly reduces the average syntax error rate to 2.63.
  • The framework increases semantic similarity between generated code and requirements to 0.91.
  • Comparative experiments demonstrate superior performance over existing code-generation methods.

Conclusions:

  • MAS4SysML offers an effective solution for generating accurate SysML v2 code, addressing key limitations of current LLM-based approaches.
  • The multi-agent, iterative repair framework improves the reliability and consistency of MBSE artifacts.
  • This work facilitates wider adoption of MBSE in complex system development by automating accurate model generation.