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相关概念视频

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...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Temperature Dependent Deformation01:12

Temperature Dependent Deformation

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In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
193
Typical Model Studies01:30

Typical Model Studies

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
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使用物理信息的神经网络对颗粒状土壤进行热力学一致的建模.

Nazanin Irani1, Mohammad Salimi2, Torsten Wichtmann2

  • 1Chair of Soil Mechanics, Foundation Engineering, and Environmental Geotechnics, Ruhr-University Bochum, Bochum, Germany. nazanin.irani@rub.de.

Scientific reports
|July 28, 2025
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概括

本研究引入了使用地质技术和物理信息神经网络 (GINN) 的颗粒土壤的新型构成模型. GINN模型确保了热力学一致性,并准确地预测了土壤的行为,优于现有的模型.

关键词:
构成模型的建构模型.节能节能节能节能节能吉恩 (Ginn) 的意思是说.基于物理学的神经网络.热力学定律 热力学定律 热力学定律

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科学领域:

  • 计算地质力学计算地质力学
  • 材料科学 材料科学 材料科学
  • 在工程领域的人工智能.

背景情况:

  • 数据驱动型号在模式识别方面表现出色,但往往缺乏物理基础和通用性.
  • 基于物理学的神经网络 (PINNs) 将管理方程集成到机器学习中,以提高物理一致性.
  • 对于颗粒状土壤的现有构成模型可能无法完全捕捉复杂的行为或遵守热力学原理.

研究的目的:

  • 为颗粒状土壤开发一种新的,热力学上一致的构成模型.
  • 利用地质技术和物理信息的神经网络 (GINN) 整合物理定律与数据驱动学习.
  • 确保模型预测遵守基本的热力学原理,包括非负散射.

主要方法:

  • 开发了一个GINN模型,其中包含一个复合损失函数,其中包括热力学可接受性约束.
  • 通过从总投入工作和自由能量潜力计算,确保非负的材料散射率.
  • 通过对各种初始空隙比和应力状态的单调排水三轴测试数据进行模型验证.

主要成果:

  • GINN模型准确地模拟了颗粒状土壤样本的剪切强度和扩展反应.
  • 预测表明与热力学定律的一致性,包括严格的非负物质消散.
  • 该模型的预测准确性与广泛采用的文献模型相提并论,在某些方面优于它们.

结论:

  • 拟议的GINN框架为模拟颗粒状土壤提供了强大的和热力学上一致的方法.
  • 将物理原理直接集成到神经网络中,提高了构成模型的可靠性和通用性.
  • 这种方法为开发先进的基于物理的机器学习模型在地力学方面提供了有前途的方向.