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相关概念视频

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

2.1K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55...
2.1K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

832
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...
832
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

719
In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
719
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

417
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
417

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相关实验视频

Updated: Jul 8, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

物理限制的深度学习用于预测水库热结构:增强的解释性和推断能力.

Jianying Song1, Jie Song1, Yujun Yi1

  • 1State Key Laboratory of Regional Environment and Sustainability, School of Environment, Beijing Normal University, Beijing, 100875, China.

Water research
|December 4, 2025
PubMed
概括

一个新的物理约束深度学习框架 (P-DL) 增强了水库热结构预测. 这种方法提高了生态保护策略的准确性和可靠性,优于传统模型.

科学领域:

  • 环境科学 环境科学
  • 水资源管理 水资源管理
  • 机器学习 机器学习

背景情况:

  • 准确的水库热结构预测对于生态保护和优化水库运营至关重要.
  • 现有的数据驱动模型在有限的数据,不良的物理解释性和不可靠的推断方面扎.
  • 挑战包括预测水温动态和理解分层.

研究的目的:

  • 提出一个物理限制的深度学习框架 (P-DL),以克服当前数据驱动模型的局限性.
  • 为了提高预测准确性,物理解释性和储水池热结构的外推稳定性.
  • 为储水池中智能热管理提供可靠的工具.

主要方法:

  • 开发了一个物理限制的深度学习框架 (P-DL).
  • 使用机制驱动的流程模型增强培训数据,并确定了关键影响因素.
  • 将垂直温度配置文件转换为可解释的参数 (A,B,D),以表示分层强度,并通过弱物理约束来改善外推.
  • 与P-DL与随机森林 (RF),支持矢量机 (SVM) 和长短期内存 (LSTM) 进行了比较,使用的是江 (XJB) 水库数据.

主要成果:

  • 与RF,SVM和LSTM相比,P-DL在预测短期局部波动方面表现出卓越的准确性.
  • 可解释的参数 (A,B,D) 有效地捕获了分层强度,峰值时间和时间演变.
关键词:
深度学习算法深度学习算法外加推断预测能力的能力.混合动力模型 混合动力模型物理一致性分析分析物理限制 物理限制储的热结构 储的热结构

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相关实验视频

Last Updated: Jul 8, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere
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Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere

Published on: April 30, 2018

Measurements of Soil Water Potential and Conductivity based on a Simple Evaporation Experiment using a Hydraulic Property Analyzer
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Measurements of Soil Water Potential and Conductivity based on a Simple Evaporation Experiment using a Hydraulic Property Analyzer

Published on: August 9, 2024

  • 在SSP5-8.5情景下,P-DL在强分层过程中获得了表面温度的高精度 (RMSE:0.83-1.1°C;R2:0.88-0.9).
  • 在本地和总体水平上,P-DL显示出优异的一致性 (KLD: 2.85-5.71;KSS: 0.2-0.4).
  • 结论:

    • 拟议的P-DL框架显著提高了预测准确性,物理解释性和储热结构的外推稳定性.
    • 该框架为水库中的智能热管理和生态保护提供了有价值的参考.
    • 混合模型和弱物理约束方法可以推进对其他环境因素的数据驱动预测.