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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

125
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,...
125
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

135
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....
135
Navier–Stokes Equations01:28

Navier–Stokes Equations

743
For incompressible Newtonian fluids, where density remains constant, stresses show a linear relationship with the deformation rate, defined by normal and shear stresses. Normal stresses depend on the pressure exerted on the fluid and the rate of deformation in specific directions, which determines how fluid flows under varying pressures. Shear stresses, on the other hand, act tangentially across fluid layers. They explain how adjacent fluid layers slide relative to one another, connecting...
743
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

399
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
399
Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

648
James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and...
648
Second Order systems II01:18

Second Order systems II

173
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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Combining Microfluidics and Microrheology to Determine Rheological Properties of Soft Matter during Repeated Phase Transitions
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基于物理信息的神经网络的时间分数微分方程反向框架,用于风病学.

Sukirt Thakur1, Harsa Mitra1, Arezoo M Ardekani1

  • 1School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.

Biology
|July 29, 2025
PubMed
概括

基于物理学的神经网络 (PINNs) 现在通过时间分数导数来解决反向问题. 这种数据效率高的方法准确地模拟复杂的系统,如异常扩散和粘弹性,即使有噪音数据.

科学领域:

  • * 计算数学和物理.
  • * 复杂动态系统的建模.

背景情况:

  • * 时间分数微分方程模拟生物传输和粘性弹性中的依赖记忆的动态.
  • *用这些方程解决反向问题是具有挑战性的,因为稳定性,独特性和数据限制.
  • *现有的物理信息神经网络 (PINNs) 通常仅限于整数顺序的导数.

研究的目的:

  • * 开发一个PINN框架,用于时间分数导数的反向问题.
  • *将框架应用于异常扩散和分数粘弹性.
  • *从合成和实验数据中推断关键物理参数.

主要方法:

  • * 开发了一个定制的PINN框架,用于时间分数反向问题.
  • * 嵌入了缩放的残余损失函数,以提高对噪声的强度.
  • *使用合成数据集和来自猪组织的实验数据验证了该方法.

主要成果:

  • * 在扩散模型中成功推断了一般化扩散系数和分数导数顺序.
  • * 在分数麦克斯韦尔模型中准确地恢复了放松参数.
  • *即使在25%的高斯噪声下,也实现了参数恢复的10%以下的相对误差.
  • * 在猪组织实验中证明了放松模块的准确预测.
关键词:
异常扩散的异常扩散分数建模的分数建模.基于物理的机器学习.类风病学 类风病学 类风病学 类风病学

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结论:

  • *开发的PINN框架有效地从杂和稀疏的数据中学习分数动态.
  • * 这种方法对模拟复杂的生物和机械系统具有显著的前景.
  • * 这项工作将PINNs的适用性扩展到更广泛的分数微分方程类别.