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相关概念视频

Sampling Methods: Overview01:06

Sampling Methods: Overview

390
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
390
Parallel Processing01:20

Parallel Processing

187
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
187
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Sampling Plans01:23

Sampling Plans

220
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
220
Random Sampling Method01:09

Random Sampling Method

11.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

301
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
301

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相关实验视频

Updated: Jul 28, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

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适应性采样配置交互方法的并行分布式内存实现.

David B Williams-Young1, Norm M Tubman2, Carlos Mejuto-Zaera3

  • 1Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.

The Journal of chemical physics
|June 1, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了适应性采样配置交互 (ASCI) 的新并行实现,即选择性配置交互 (sCI) 方法. 高效的并行实现了量子系统迄今为止最大的变量ASCI计算.

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Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points
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Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points

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相关实验视频

Last Updated: Jul 28, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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An Unbiased Approach of Sampling TEM Sections in Neuroscience

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Patterned Photostimulation with Digital Micromirror Devices to Investigate Dendritic Integration Across Branch Points
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科学领域:

  • 量子化学是一种量子化学.
  • 计算物理学的计算物理.
  • 高性能计算的高性能计算.

背景情况:

  • 量子系统模拟利用多种方法,其中合集群和选择配置交互 (sCI) 是突出的.
  • 高性能计算 (HPC) 的进步推动了许多量子模拟方法的适应.
  • 对大规模并行架构的sCI方法的开发仍然是一个未经探索的领域.

研究的目的:

  • 介绍SCI方法的自适应采样配置交互 (ASCI) 方法的并行分布式内存实现.
  • 在ASCI中解决决定性搜索,选择,哈密尔顿式形成和自值计算中的关键并行化挑战.
  • 在并行计算资源上实现更大,更复杂的量子系统模拟.

主要方法:

  • 适应性采样配置交互 (ASCI) 方法的并行分布式内存实现.
  • 在决定性搜索过程中,在负载平衡中应用记忆效率的决定性约束.
  • 在大规模并行系统上使用ASCI方法的变异自值计算.

主要成果:

  • 在最多16384个CPU上,证明了ASCI计算的近最佳加速度.
  • 成功地执行了迄今为止最大的Cr2分子 (24电子,30轨道) 的变化ASCI计算,涉及多达3x10^8的决定因素.
  • 验证了并行ASCI实施的效率和可扩展性.

结论:

  • 开发的平行ASCI实现显著提升了模拟大型量子系统的能力.
  • 这项工作克服了关键的并行化障碍,使sCI方法在HPC环境中更容易使用.
  • 提出的方法为解决更大,更复杂的量子力学问题铺平了道路.