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相关概念视频

Fast Fourier Transform01:10

Fast Fourier Transform

270
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
270
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

224
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
224
Discrete Fourier Transform01:15

Discrete Fourier Transform

219
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
219
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

267
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
267
Accelerating Fluids01:17

Accelerating Fluids

1.0K
When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
1.0K
Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

360
The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the...
360

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相关实验视频

Updated: Jun 5, 2025

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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没有中断的加速:DFT软件作为服务.

Fusong Ju1, Xinran Wei1, Lin Huang1

  • 1Microsoft Research AI for Science, Beijing 100080, China.

Journal of chemical theory and computation
|December 11, 2024
PubMed
概括
此摘要是机器生成的。

加快DFT是一种新的云应用程序,使用云基础设施和GPU显著加快密度函数理论 (DFT) 模拟. 这通过提供更快,更准确,更可扩展的DFT计算来增强计算化学和材料科学研究.

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Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
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相关实验视频

Last Updated: Jun 5, 2025

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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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科学领域:

  • 计算化学计算化学
  • 材料科学 材料科学 材料科学
  • 计算物理 计算物理

背景情况:

  • 密度函数理论 (DFT) 是科学研究的一个基本工具.
  • 计算能力和理论的进步改善了DFT.
  • 越来越多的DFT计算需求需要更有效的方法.

研究的目的:

  • 介绍加速DFT,这是一个新的云原生应用程序.
  • 在DFT模拟中实现显著的加速.
  • 为DFT计算提供可扩展和用户友好的解决方案.

主要方法:

  • 使用最先进的云基础设施.
  • 对图形处理单元 (GPU) 的重新设计算法.
  • 实现用于DFT模拟的云原生应用程序.

主要成果:

  • 在DFT模拟中实现了数量级加速.
  • 在高速计算中保持准确性.
  • 在不牺牲准确性的情况下,演示了高速计算.

结论:

  • 加快DFT为DFT模拟提供了显著的加快速度.
  • 该应用程序是用户友好和可扩展的.
  • 加快的DFT可以在各种领域加快科学发现.