相关实验视频
Updated: Jun 8, 2025

09:17
Surrogate Model Development for Digital Experiments in Welding
Published on: March 28, 2025
708
基于不确定性的积极学习的表现,以有效地近似材料科学中的黑子函数
Ai Koizumi1, Guillaume Deffrennes2, Kei Terayama3,4,5
1Center for Basic Research on Materials, National Institute for Materials Science, 1-1, Namiki, Tsukuba, Ibaraki, 305-0044, Japan. koizumi.ai@nims.go.jp.
Scientific reports
|November 6, 2024
概括
使用不确定性采样进行主动学习,可以提高材料科学回归任务中的黑子函数近似度,特别是在低维空间. 然而,它的效率在材料数据库中常见的高维,不平衡的数据下降.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 接近黑子功能对于评估新材料至关重要.
- 主动学习 (AL) 旨在通过使用最小的训练数据来提高函数近似度.
- 不确定性抽样是一种关键的AL策略.
研究的目的:
- 评估基于不确定性的积极学习在材料科学回归中的近似黑子函数的效率.
- 将AL性能与不同材料数据集和维度的随机抽样进行比较.
主要方法:
- 在回归任务中研究了基于不确定性的积极学习 (AL).
- 利用各种材料数据库,包括三元系统,无机材料,小分子和聚合物.
- 基于输入数据分布 (均与离散/不平衡) 和特征空间维度的评估性能.
主要成果:
- 基于不确定性的AL在低维,均的输入空间 (例如液体表面) 中优于随机抽样.
- 在材料数据库中典型的高维,不平衡的特征空间中,AL的效率下降.
- 性能与材料描述器维度相关;较低的维度有利于AL.
结论:
- 基于不确定性的积极学习对材料科学来说是有前途的,但并不普遍有效.
- AL的有效性高度依赖于数据特征,如维度和分布.
- 需要进一步的研究来优化对复杂材料数据的AL策略.
相关概念视频
Accuracy, limits, and approximation
441
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
441
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
42
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...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
42
Linear Approximation in Time Domain
64
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
64
Propagation of Uncertainty from Random Error
654
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...
654
Propagation of Uncertainty from Systematic Error
485
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...
485
Uncertainty: Overview
526
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.
526

