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

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

This article presents a protocol and software tool for the quantification of uncertainties in the calibration and data analysis of a semi-continuous thermal-optical organic/elemental carbon...

The Uncertainty Principle

The Uncertainty Principle

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...

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

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...

Uncertainty: Overview

Uncertainty: Overview

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.

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

To date research has focused on cognitive strategies people adopt to cope with uncertainty. This research examines instead an experiential way of dealing with uncertainty and introduces a set of experimental methods showing how the experience of haptic softness can serve as a tool to deal with...

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.

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相关实验视频

Updated: Jan 20, 2026

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

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

Published on: September 7, 2019

VENI,VINDy,VICI:一个具有不确定性定量化的生成性减少顺序建模框架.

Paolo Conti¹, Jonas Kneifl², Andrea Manzoni¹

¹MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy.

Neural networks : the official journal of the International Neural Network Society

|January 18, 2026

概括

此摘要是机器生成的。

我们为生成模型引入了一个新的框架,以确保科学预测的物理一致性. 这种方法将数据驱动的方法与概率建模集成为准确的,不确定性意识的减少顺序模型.

关键词:

数据驱动的方法数据驱动的方法生成性AI是一种人工智能.非线性动力学是一种非线性动力学.减少的订单建模减少的订单建模稀少的系统识别标识.变化自动编码器的变化自动编码器

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Heisenberg's Uncertainty Principle

Heisenberg's Uncertainty Principle

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

相关实验视频

Last Updated: Jan 20, 2026

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

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

Published on: September 7, 2019

Heisenberg's Uncertainty Principle

Heisenberg's Uncertainty Principle

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

科学领域:

计算科学计算科学
基于物理的机器学习
数据驱动建模数据驱动建模

背景情况:

生成模型提供高效的场景探索,但往往缺乏物理一致性.
计算科学依赖于物理一致性来进行可靠的预测.
现有的模型难以平衡数据驱动的洞察力与物理定律.

研究的目的:

开发一种新的物理生成框架,用于创建物理一致的减少顺序模型.
将数据驱动的系统识别与不确定性量化的概率建模集成在一起.
通过确保模型可靠性,提高复杂物理现象的决策能力.

主要方法:

VENI (噪声输入的变量编码):使用变量自编码器来识别从高维,噪声数据中的缩小坐标.
VINDy (非线性动态的变量识别):将稀疏系统识别扩展到概率建模以发现系统动态.
VICI (具有可信度区间的变量推理):能够有效地生成全职解决方案,并提供不确定性量化.

主要成果:

拟议的框架成功地构建了物理一致的减少顺序模型.
证明了对未见的参数和初始条件的有效不确定性量化.
在各种系统中验证了性能,包括混乱和高维非线性动态.

结论:

VENI,VINDy,VICI框架为物理一致的生成建模提供了一个强大的解决方案.
这种方法提高了生成模型在科学和工程中的可靠性和适用性.
它为更加可靠和高效的复杂物理系统的计算探索铺平了道路.