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Updated: Sep 13, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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基于物理学的神经网络与未知偏微分方程:在多变量时间序列中的应用

Seyedeh Azadeh Fallah Mortezanejad1, Ruochen Wang1, Ali Mohammad-Djafari2,3

  • 1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.

Entropy (Basel, Switzerland)
|July 29, 2025
PubMed
概括

本研究介绍了从数据中自动发现治理方程的方法,将其集成到物理信息神经网络 (PINNs) 和贝叶斯方法中. 这提高了复杂系统的预测准确性,即使信息不完整.

关键词:
贝叶斯计算是贝叶斯的计算.多变量时间序列 (MTS)部分微分方程 (PDEs) 的方法基于物理学的神经网络 (PINN)

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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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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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...
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