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関連する概念動画

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

8.1K
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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The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.2K
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Overview of Systemic and Pulmonary Circulation01:15

Overview of Systemic and Pulmonary Circulation

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The systemic and pulmonary circuits are crucial components of the circulatory system, working together to transport blood between the heart, lungs, and the rest of the body. The process begins with pulmonary circulation, where deoxygenated blood is pumped from the right ventricle to the lungs via the pulmonary trunk and arteries. Upon reaching the lungs, the blood becomes oxygenated and returns to the heart, specifically to the left atrium, via the pulmonary veins.
The oxygenated blood is sent...
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Uncertainty: Overview00:59

Uncertainty: Overview

1.8K
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.
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関連する実験動画

Updated: Feb 15, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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素早いパラメータ推定と不確実性定量化のためのシステム循環の物理情報エミュレーション

William Ryan1, Alyssa Taylor-LaPole2, Mette Olufsen3

  • 1School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.

International journal for numerical methods in biomedical engineering
|February 14, 2026
PubMed
まとめ
この要約は機械生成です。

この研究は,血管ネットワークの血流を予測するために,物理情報に基づいたニューラルネットワークを使用して,より速い機械学習モデルを導入しています. この方法は,ダブルアウトレット右心室 (DORV) のような状態の患者特有の効率的な校正を可能にします.

キーワード:
FALD (ファルド) とはフォントン・フォンタンは,計算式流体力学についてパーフュージョン・パーフュージョンとはウォール・シーアー・ストレスのストレス

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

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科学分野:

  • 計算式流体ダイナミクス
  • バイオメディカルエンジニアリング
  • 医療における機械学習

背景:

  • 血流の計算モデルは不可欠ですが,繰り返しシミュレーションを必要とする臨床アプリケーションでは計算コストが高くなります.
  • 患者特有のパラメータ推定とモデルの校正には,効率的なシミュレーション方法が必要です.

研究 の 目的:

  • 血管ネットワークにおける迅速で患者特有の血流と血圧の予測のための物理情報に基づくニューラルネットワークの枠組みを開発する.
  • 臨床応用のための効率的なパラメータ推論と逆不確実性定量化を可能にする.

主な方法:

  • 代替モデリングアプローチとして,物理情報ニューラルネットワーク (PINNs) を利用しました.
  • 血管ネットワークの患者特有のモデル校正に焦点を当てた.
  • このフレームワークを,二重出口右心室 (DORV) の患者からの臨床データに適用した.

主要な成果:

  • 訓練された機械学習モデルは,従来の数値解析器と比較して計算時間を大幅に短縮します.
  • 流量と圧力波形の正確な予測を達成しました.
  • 代替的な機械学習方法に対する比較研究で,フレームワークの有効性を実証しました.

結論:

  • 物理情報に基づくニューラルネットワークは,患者特有の血管モデリングのための計算効率的かつ正確なソリューションを提供します.
  • 開発されたフレームワークは,より迅速なパラメータ推論と臨床環境における逆不確実性の定量化を促進します.
  • このアプローチは,DORV.のような先天性心不全の改善されたモニタリングと管理のための約束を保持しています.