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相关概念视频

An Introduction to Mechanics01:28

An Introduction to Mechanics

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Humans have been making ships, shelters, pyramids, weapons, agricultural equipment, and many more items without recording the process or theory behind them for centuries. It would be challenging to document the evolution of mechanics from its origin to the present.
According to records, the history of mechanics starts with Aristotle (384–322 BC). He related mechanics to physical theory, aiming for a universal synthesis.
Newton defined mechanics as the branch of physical science that...
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Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

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When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
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Newton's First Law: Introduction01:17

Newton's First Law: Introduction

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Motion draws our attention. Motion itself can be beautiful, causing us to marvel at the forces needed to create spectacular sights, such as that of a dolphin jumping out of the water, the flight of a bird, or the orbit of a satellite. The study of motion is kinematics, but kinematics only describes the way objects move—their velocity and acceleration. Dynamics considers the forces that affect the motion of moving objects and systems. Newton's laws of motion are the foundation of...
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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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Kinematic Equations - II01:17

Kinematic Equations - II

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The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Updated: Jun 23, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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从数据中学习动态系统:介绍物理引导的深度学习.

Rose Yu1, Rui Wang1,2

  • 1Department of Computer Science and Engineering, University of California, San Diego, CA 92093.

Proceedings of the National Academy of Sciences of the United States of America
|June 24, 2024
PubMed
概括
此摘要是机器生成的。

物理指导的深度学习 (DL) 将物理定律集成到复杂动态的数据驱动模型中. 这种方法结合了传统基于物理的模型和DL的优势,提供了改进的科学问题解决.

关键词:
人工智能用于科学科学.深度学习是一种深度学习.动态系统是一个动态系统.

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科学领域:

  • 科学建模的科学建模
  • 动态系统是动态系统.
  • 计算科学是一种计算科学.

背景情况:

  • 传统的基于物理的模型提供了可解释性,但需要大量的资源和专业知识.
  • 深度学习 (DL) 模型是高效的,但需要大量的数据,可能违反物理定律.
  • 现有的方法在复杂的动态建模中努力平衡准确性,可解释性和数据效率.

研究的目的:

  • 引入针对动态系统量身定制的物理引导深度学习 (DL) 的框架.
  • 在这个框架内对最先进的方法进行分类和分析.
  • 确定用于科学应用的物理引导DL的挑战和机遇.

主要方法:

  • 将第一原则的物理知识纳入数据驱动的DL方法.
  • 开发一个专门针对动态系统的学习管道.
  • 对当前以物理为导向的DL技术进行系统审查和分类.

主要成果:

  • 证明了物理引导DL的潜力,以克服传统和纯DL方法的局限性.
  • 提供了现有的现场方法的结构化概述.
  • 强调了将物理定律与数据驱动学习相结合的协同效益.

结论:

  • 物理引导的DL为建模复杂的物理动态提供了一个强大的范式.
  • 该框架有助于开发更可靠,可解释和高效的科学模型.
  • 未来的研究方向包括解决这个快速发展的领域的挑战和探索新机遇.