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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Linear Momentum in Control Volume01:13

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Newton's second law is applied to obtain the linear momentum in a control volume in a fluid system. According to this law, the rate of change of linear momentum is equal to the sum of external forces acting on the system. When a control volume matches the fluid system at a specific moment, the forces acting on both are identical. Reynolds transport theorem helps explain this by breaking down the system's linear momentum into two components: the rate of change of linear momentum within...
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Transfer Function to State Space01:23

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
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Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

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Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured...
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Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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相关实验视频

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Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging
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一种转移学习方法来解决基于等效线性化的福克-普朗克方程.

Gege Wang1, Xiaolong Wang1,2, Qi Liu3

  • 1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China.

Chaos (Woodbury, N.Y.)
|August 8, 2025
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概括

本研究介绍了一种新的转移学习方法,以有效地解决随机系统的福克-普朗克 (FP) 方程. 这种方法加快了计算速度,并保持了复杂系统的准确性.

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科学领域:

  • 计算物理 计算物理
  • 应用数学 应用数学 应用数学
  • 机器学习 机器学习

背景情况:

  • 解决福克-普朗克 (FP) 方程对于分析随机系统至关重要.
  • 当前的方法可能是计算密集型,限制了它们的应用.
  • 需要更高效,更准确的解决方案技术.

研究的目的:

  • 开发一种基于转移学习的有效方法来解决福克-普朗克方程.
  • 为了加速解决复杂的随机系统的培训过程.
  • 证明方法的准确性和概括能力.

主要方法:

  • 相当的线性化来统一随机微分方程.
  • 一个预训练的神经网络框架,灵感来自转移学习.
  • 用高斯和莱维噪声对一维和二维系统进行数值实验.

主要成果:

  • 拟议的转移学习方法显著减少了解决FP方程的培训时间.
  • 该方法准确地学习FP方程的轮.
  • 对于高斯和莱维噪声系统有效.

结论:

  • 转移学习方法为福克-普朗克方程提供了一个计算效率高的解决方案.
  • 该方法在不同的随机系统中表现出强大的概括能力.
  • 这种技术通过提高计算效率来增强对随机系统的研究.