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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
515
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
471
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Combustion Energy: A Measure of Stability in Alkanes and Cycloalkanes02:14

Combustion Energy: A Measure of Stability in Alkanes and Cycloalkanes

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The low reactivity in alkanes can be attributed to the non-polar nature of C–C and C–H σ bonds. Alkanes, therefore, were  initially termed as “paraffins,” derived from the Latin words: parum, meaning “too little,” and affinis, meaning “affinity.”
Alkanes undergo combustion in the presence of excess oxygen and high-temperature conditions to give carbon dioxide and water. A combustion reaction is the energy source in natural gas, liquified...
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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相关实验视频

Updated: Jun 4, 2025

Combustion Chemistry of Fuels: Quantitative Speciation Data Obtained from an Atmospheric High-temperature Flow Reactor with Coupled Molecular-beam Mass Spectrometer
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在规模的合野火-大气模拟中的不确定性量化.

Paul Schwerdtner1, Frederick Law1, Qing Wang2

  • 1Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA.

PNAS nexus
|December 23, 2024
PubMed
概括

利用受过相关数据训练的代用模型显著加速野火模拟,以更好地量化不确定性. 这种方法大大减少了计算时间,并提高了预测野火影响的准确性.

关键词:
多忠实度方法多忠实度方法神经网络的神经网络的神经网络代孕模拟的代孕模拟不确定性量化不确定性量化野火模拟的野火模拟

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科学领域:

  • 计算科学是一种计算科学.
  • 环境建模环境建模
  • 野火的动态 野火的动态

背景情况:

  • 野火模拟对于消防管理和疏散规划至关重要.
  • 在高保真性野火模型中量化不确定性是计算上昂贵的.
  • 目前的方法与现代野火的规模和强度作斗争.

研究的目的:

  • 在野火模拟中开发一种可扩展的多真实性方法来量化野火模拟中的不确定性.
  • 为了证明在相关数据上训练的代孕模型的有效性.
  • 为了降低野火不确定性量化计算成本.

主要方法:

  • 利用在偏见但相关的数据上训练的代孕模型.
  • 实施多忠实度方法,结合代用模型和高忠实度模型.
  • 将该方法应用于数十亿自由度的大规模野火模拟.

主要成果:

  • 将不确定性定量化的培训时间缩短了几个数量级 (从3个月减少到3小时以下).
  • 在固定预算内,与高保真模拟相比,在烧毁区域预测中至少达到两倍的准确性.
  • 证明了对大规模野火场景的多忠度不确定性量化的实用性.

结论:

  • 在相关数据上训练的替代模型对于计算上昂贵的模拟是有效的.
  • 相关性,而不是偏差,是加速多忠实性方法中不确定性量化的关键.
  • 该方法为野火模拟不确定性提供了一个可扩展的解决方案,并在科学计算中具有更广泛的应用.