Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

213
Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
213
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

808
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
808

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Chip-Scale Aligned Chiral Carbon Nanotubes Exhibiting Giant Second Harmonic Generation.

ACS nano·2026
Same author

Author Correction: Peptide design through binding interface mimicry with PepMimic.

Nature biomedical engineering·2025
Same author

Dynamic evolution of water conducting fracture zones and roof water hazard early warning based on 3d spatial clustering.

Scientific reports·2025
Same author

Peptide design through binding interface mimicry with PepMimic.

Nature biomedical engineering·2025
Same author

Linear and passive silicon-on-insulator refractive index sensor utilizing Bragg grating-assisted Michelson interferometer.

Optics express·2025
Same author

Planar p-n Junction Engineering toward Reconfigurable Organic Synaptic Transistors for High-Accuracy Neuromorphic Recognition.

Small (Weinheim an der Bergstrasse, Germany)·2025

相关实验视频

Updated: Jul 6, 2025

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator
07:42

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Published on: December 15, 2021

3.1K

基于物理学的循环神经网络用于光学共振中的时间动态.

Yingheng Tang1,2, Jichao Fan1, Xinwei Li3

  • 1Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA.

Nature computational science
|January 4, 2024
PubMed
概括

这项研究引入了基于物理的循环神经网络,以快速预测光学共振动态和频率. 新型机器学习方法通过分析部分数据序列来加速物理探索和设备设计.

更多相关视频

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity
11:30

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

11.7K
Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.1K

相关实验视频

Last Updated: Jul 6, 2025

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator
07:42

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Published on: December 15, 2021

3.1K
Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity
11:30

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

11.7K
Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.1K

科学领域:

  • 光学科学 光学科学
  • 机器学习 机器学习
  • 基于物理学的神经网络.

背景情况:

  • 光学共振现象在各种科学领域至关重要.
  • 目前用于捕获共振时间动态的方法受到长时间的采集时间和低准确度的限制.

研究的目的:

  • 开发一种新的机器学习模型,用于准确和高效地预测光学共振时间域响应.
  • 从部分时间序列数据推断共振频率.

主要方法:

  • 开发了一个以物理为基础的循环神经网络 (RNN).
  • 采用了两步,多忠度的培训框架,利用合成和特定应用的数据.
  • 该模型通过模拟和实验来验证.

主要成果:

  • 该模型使用输入序列的一小部分准确预测光学共振的时间域响应.
  • 它成功地在各种系统中推断了共振频率,包括介电元表面,石墨烯等离子体和兰道极子.
  • 该算法捕获了微妙的信号特征,并学习了潜在的物理量.

结论:

  • 开发的机器学习算法显著加速了对共振增强光物质相互作用的研究.
  • 它为高效的物理现象探索和光学设备设计提供了强大的工具.