tensor

简明释义

英[ˈtensə(r);ˈtensɔː(r)]美[ˈtensərˌˈtensɔːr]

n. [数] 张量；[解剖] 张肌

英英释义

单词用法

同义词

反义词

例句

1.Tensor algebra is tied to coordinates.

张量代数则离不开坐标系。

2.Tensor algebra is tied to coordinates .

张量代数则离不开坐标系。

3.An invariant formula for the right stretch tensor rate is given by a differential method directly.

利用直接求微分的方法，给出了右伸长张量率的不变性表示公式。

4.However, another alternative theory, called tensor-vector-scalar gravity (TeVeS), does conflict with the new findings.

可是，另外还有一个可能取代广义相对论的理论称为张量-矢量-标量引力(TeVeS)了，它与我们的新发现有冲突。

5.The special M.R.I. technique, known as diffusion tensor imaging, is also being studied to help improve the diagnosis of concussions.

这种特殊形式的核磁共振成像技术被称为弥散张量成像，对这种技术的也用来提高诊断脑震荡的精确度。

6.Objective: To quantify the characteristic of cerebral gliomas by using diffusion-tensor MR imaging.

目的：应用弥散张量磁共振成像定量分析大脑胶质瘤的特点。

7.When training a neural network, we often manipulate tensors 张量 to optimize the model's performance.

在训练神经网络时，我们通常操作 tensors 张量 来优化模型性能。

8.The output of a convolutional layer in a CNN is a new tensor 张量 that captures features from the input.

CNN 中卷积层的输出是一个新的 tensor 张量，它捕捉了输入的特征。

9.A 3D tensor 张量 can represent an image with width, height, and color channels.

一个 3D tensor 张量 可以表示具有宽度、高度和颜色通道的图像。

10.In machine learning, a tensor 张量 is a fundamental data structure used for representing multi-dimensional arrays.

在机器学习中，tensor 张量 是用于表示多维数组的基本数据结构。

11.In physics, a stress tensor 张量 describes the internal forces within a material.

在物理学中，压力 tensor 张量 描述材料内部的力。

作文

In the realm of mathematics and physics, the concept of a tensor (张量) plays a crucial role in understanding complex systems and phenomena. A tensor is a mathematical object that generalizes the concepts of scalars, vectors, and matrices. It can be thought of as a multi-dimensional array of numerical values that transforms according to certain rules when the coordinate system is changed. This property makes tensors particularly useful in fields such as general relativity, fluid dynamics, and machine learning.To grasp the significance of tensors, one must first understand their simpler counterparts. A scalar is a single number, like temperature or mass. A vector, on the other hand, is an ordered list of numbers that represents both magnitude and direction, such as velocity or force. When we move to matrices, we deal with two-dimensional arrays that can represent transformations in space, like rotation or scaling.However, many physical phenomena cannot be adequately described by just scalars, vectors, or matrices. This is where tensors come into play. For example, in the study of stress in materials, we use a second-order tensor to represent the internal forces acting within a material. This tensor captures how these forces vary with direction, providing a comprehensive picture of the stress state.In general relativity, Einstein famously used tensors to describe the curvature of spacetime caused by mass and energy. The Einstein field equations relate the geometry of spacetime, represented by a tensor known as the metric tensor, to the distribution of matter and energy, encapsulated in the stress-energy tensor. This interplay between geometry and physics illustrates the power of tensors in formulating theories that describe the universe.Moreover, in the field of machine learning, tensors have gained prominence due to their ability to handle multi-dimensional data. Deep learning models often utilize tensors to process inputs that have multiple features, such as images or text. In this context, a three-dimensional tensor might represent an image with width, height, and color channels, allowing algorithms to learn patterns and make predictions based on complex datasets.The versatility of tensors is further enhanced by the various operations that can be performed on them, such as addition, multiplication, and contraction. These operations allow for the manipulation and transformation of tensors in ways that are analogous to operations on vectors and matrices, but with greater complexity and richness.In conclusion, the study of tensors (张量) is essential for anyone interested in advanced mathematics, physics, or data science. Their ability to encapsulate and describe multi-dimensional relationships makes them indispensable tools in modern scientific inquiry. As we continue to explore the intricacies of the universe and the vast amounts of data generated in our digital age, the importance of tensors will only grow, paving the way for new discoveries and innovations across various disciplines.