interpolations
简明释义
英[ˌɪntərpəˈleɪʃənz]美[ˌɪntərpəˈleɪʃənz]
n. 孤起部；篡改；插入（interpolation 的复数）
英英释义
单词用法
插值法；内插法；插补方法 | |
线性插值；线性内插法；直线切削 |
同义词
反义词
例句
1.Secondly, the global coordinates and stress values of the points are calculated by using standard isoparametric interpolations, the node coordinates and the known node stresses.
第二，用单元的结点坐标、结点应力以及等参插值函数，插值计算这些点的总体坐标及应力。
2.Without repeated interpolations such as in ELM, the Particle-tracing Method reduces the numerical diffusion quite a lot.
因为没有像欧拉·拉格朗日方法那样反复插值，所以质点跟踪法极大地降低了数值耗散。
3.Test conditions for machining centres - Part 6: Accuracy of feeds, speeds and interpolations; Technical Corrigendum 1.
加工中心检验条件。第6部分：进给率、速度和插补精度。技术勘误1。
4.Secondly, the global coordinates and stress values of the points are calculated by using standard isoparametric interpolations, the node coordinates and the known node stresses.
第二，用单元的结点坐标、结点应力以及等参插值函数，插值计算这些点的总体坐标及应力。
5.The interpolations are made along the time-line with varying reach-back time distance, rather than the more widely used spatial interpolations.
结点上的变量值采用时间序列回退内插的新的插值格式，而不是传统的空间内插方法。
6.Note: Interpolations, possibly added by others, are indicated in parenthesis.
注：括号中话可能是后人添加的。
7.Several interpolation methods are discussed and compared, and Fourier description is introduced as a interpolations.
论文讨论和比较了几种插值方法，引入傅立叶描述子对轮廓线进行插值；
8.Using interpolations, we can predict future trends based on past data.
通过使用插值，我们可以根据过去的数据预测未来的趋势。
9.The software includes tools for performing interpolations on large datasets efficiently.
该软件包括有效对大型数据集进行插值的工具。
10.The scientist applied various interpolations to the temperature data to create a smoother graph.
科学家对温度数据应用了多种插值方法，以创建更平滑的图表。
11.In graphics programming, interpolations help in rendering smooth transitions between frames.
在图形编程中，插值有助于渲染帧之间的平滑过渡。
12.In data analysis, interpolations are often used to estimate missing values in a dataset.
在数据分析中，插值通常用于估算数据集中缺失的值。
作文
In the realm of mathematics and data analysis, the concept of interpolations plays a crucial role in understanding and predicting values within a given dataset. Interpolations refer to the method of estimating unknown values that fall between known values in a dataset. This technique is widely used in various fields such as engineering, computer graphics, and even in everyday applications like weather forecasting.To illustrate the importance of interpolations, consider a simple example involving temperature readings taken at specific times throughout the day. Suppose we have recorded the temperature at 8 AM, 12 PM, and 4 PM. However, we want to know the temperature at 10 AM, a time for which we do not have direct data. By utilizing interpolations, we can estimate the temperature at 10 AM based on the known values from 8 AM and 12 PM. This allows us to create a more complete picture of the temperature changes throughout the day.There are different methods of performing interpolations, including linear interpolation, polynomial interpolation, and spline interpolation. Linear interpolation is the simplest form, connecting two known points with a straight line. Polynomial interpolation, on the other hand, uses polynomials to connect multiple points, providing a smoother curve that can fit the data more accurately. Spline interpolation involves piecewise polynomials, which can offer greater flexibility and precision when dealing with complex datasets.The application of interpolations extends beyond just numerical data. In computer graphics, for example, interpolations are used to render images more smoothly by estimating pixel colors based on surrounding pixels. This technique enhances the visual quality of images and animations, making them more appealing to viewers.Moreover, in scientific research, interpolations are essential for analyzing experimental data. Researchers often collect data points that may not cover every possible value in their studies. By applying interpolations, they can fill in gaps and make predictions about phenomena that have not yet been observed directly.Despite its widespread use, it is important to note that interpolations come with certain limitations. The accuracy of the estimated values heavily depends on the method used and the distribution of the known data points. For instance, using linear interpolations might not be suitable for datasets that exhibit non-linear behavior, as it could lead to significant errors in estimation. Therefore, understanding the nature of the data and selecting the appropriate interpolation method is crucial for achieving reliable results.In conclusion, interpolations serve as a powerful tool in various domains, allowing us to estimate unknown values based on known information. Whether in mathematics, computer graphics, or scientific research, the ability to perform interpolations enhances our understanding of data and enables better decision-making. As technology continues to evolve, the significance of interpolations will undoubtedly grow, paving the way for more advanced analytical techniques and applications in the future.
在数学和数据分析领域，插值法的概念在理解和预测给定数据集中的值方面发挥着至关重要的作用。插值法是指估计落在数据集中已知值之间的未知值的方法。这种技术广泛应用于工程、计算机图形学，甚至日常应用如天气预报等多个领域。为了说明插值法的重要性，考虑一个简单的例子，涉及一天中特定时间的温度读数。假设我们在上午8点、中午12点和下午4点记录了温度。然而，我们想知道上午10点的温度，而这段时间我们没有直接的数据。通过利用插值法，我们可以根据上午8点和中午12点的已知值来估算上午10点的温度。这使我们能够更全面地了解一天中温度的变化。执行插值法的方法有多种，包括线性插值、多项式插值和样条插值。线性插值是最简单的形式，用一条直线连接两个已知点。另一方面，多项式插值使用多项式来连接多个点，从而提供更平滑的曲线，可以更准确地拟合数据。样条插值涉及分段多项式，在处理复杂数据集时可以提供更大的灵活性和精确度。插值法的应用不仅限于数值数据。在计算机图形学中，例如，插值法用于通过根据周围像素估算像素颜色来更平滑地渲染图像。这种技术增强了图像和动画的视觉质量，使其对观众更具吸引力。此外，在科学研究中，插值法对于分析实验数据至关重要。研究人员通常收集的数据点可能并未覆盖他们研究中的每个可能值。通过应用插值法，他们可以填补空白，并对尚未直接观察到的现象做出预测。尽管使用广泛，但需要注意的是，插值法存在某些局限性。估计值的准确性在很大程度上依赖于所用方法和已知数据点的分布。例如，使用线性插值法可能不适合表现非线性行为的数据集，因为这可能导致估计中出现重大误差。因此，理解数据的性质并选择合适的插值方法对于实现可靠结果至关重要。总之，插值法作为一种强大的工具在各个领域发挥作用，使我们能够根据已知信息估计未知值。无论是在数学、计算机图形学还是科学研究中，执行插值法的能力增强了我们对数据的理解，并促进了更好的决策。随着技术的不断发展，插值法的重要性无疑会不断增长，为未来更先进的分析技术和应用铺平道路。
