Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Double Resonance Techniques: Overview

Double Resonance Techniques: Overview

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...

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

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

Search research articles

Related Experiment Video

Updated: Jul 6, 2025

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Published on: December 15, 2021

Physics-informed recurrent neural network for time dynamics in optical resonances.

Yingheng Tang^1,2, Jichao Fan¹, Xinwei Li³

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

Nature Computational Science

|January 4, 2024

Summary

This summary is machine-generated.

This study introduces a physics-informed recurrent neural network to rapidly forecast optical resonance dynamics and frequencies. The novel machine learning approach accelerates physical exploration and device design by analyzing partial data sequences.

More Related Videos

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

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

Published on: March 6, 2017

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

Related Experiment Videos

Last Updated: Jul 6, 2025

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Published on: December 15, 2021

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

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

Published on: March 6, 2017

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

Area of Science:

Optical Science
Machine Learning
Physics-Informed Neural Networks

Background:

Optical resonance phenomena are crucial in various scientific fields.
Current methods for capturing resonance time dynamics are limited by long acquisition times and low accuracy.

Purpose of the Study:

To develop a novel machine learning model for accurate and efficient forecasting of optical resonance time-domain responses.
To infer resonance frequencies from partial time-series data.

Main Methods:

A physics-informed recurrent neural network (RNN) was developed.
A two-step, multi-fidelity training framework was employed, utilizing synthetic and application-specific data.
The model was validated through simulations and experiments.

Main Results:

The model accurately forecasts time-domain responses of optical resonances using a fraction of the input sequence.
It successfully infers resonance frequencies across diverse systems, including dielectric metasurfaces, graphene plasmonics, and Landau polaritons.
The algorithm captures subtle signal features and learns underlying physical quantities.

Conclusions:

The developed machine learning algorithm significantly accelerates the study of resonance-enhanced light-matter interactions.
It offers a powerful tool for efficient physical phenomena exploration and optical device design.