Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Magnetic Field due to Moving Charges01:23

Magnetic Field due to Moving Charges

10.5K
A stationary charge creates and interacts with the electric field, while a moving charge creates a magnetic field.
Consider a point charge moving with a constant velocity. Like the electric field, the magnetic field at any point is directly proportional to the magnitude of the charge and inversely proportional to the square of the distance between the source point and the field point. However, unlike the electric field, the magnetic field is always perpendicular to the plane containing the line...
10.5K
Ferromagnetism01:31

Ferromagnetism

2.6K
Materials like iron, nickel, and cobalt consist of magnetic domains, within which the magnetic dipoles are arranged parallel to each other. The magnetic dipoles are rigidly aligned in the same direction within a domain by quantum mechanical coupling among the atoms. This coupling is so strong that even thermal agitation at room temperature cannot break it. The result is that each domain has a net dipole moment. However, some materials have weaker coupling, and are ferromagnetic at lower...
2.6K
Magnetic Field Due To A Thin Straight Wire01:28

Magnetic Field Due To A Thin Straight Wire

5.5K
Consider an infinitely long straight wire carrying a current I. The magnetic field at point P at a distance a from the origin can be calculated using the Biot-Savart law.
5.5K
Force On A Current Loop In A Magnetic Field01:17

Force On A Current Loop In A Magnetic Field

3.6K
Magnetic forces on wires carrying current are most frequently applied in motors. A DC motor is a device that converts electrical energy into mechanical work. In motors, wire loops are enclosed in a magnetic field. When current flows through the loops, the magnetic field applies torque, which causes the shaft to rotate. The direction of the current is reversed once the loop's surface area is lined up with the magnetic field, causing a constant torque on the loop. During the process,...
3.6K
Magnetic Field Of A Current Loop01:16

Magnetic Field Of A Current Loop

5.5K
Consider a circular loop with a radius a, that carries a current I. The magnetic field due to the current at an arbitrary point P along the axis of the loop can be calculated using the Biot-Savart law.
5.5K
Magnetic Vector Potential01:15

Magnetic Vector Potential

919
In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
919

You might also read

Related Articles

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

Sort by
Same author

Identification of drug candidates for rescue of SOX17 gene targets in pulmonary arterial hypertension.

bioRxiv : the preprint server for biology·2026
Same author

Does Support Meet the Need? A Focus Group Study on Parental Support and Students' Psychological Need Satisfaction in a Minority School Context.

Healthcare (Basel, Switzerland)·2026
Same author

Metrics for spin-based computing.

Nature reviews. Physics·2026
Same author

Beyond Subject-Specific Models in Dynamical Human-Machine Interaction: Benchmarking and Optimization Strategies.

IEEE transactions on neural networks and learning systems·2026
Same author

Predicting physics efficiently with hybrid hardware.

Nature computational science·2025
Same author

Noise-aware training of neuromorphic dynamic device networks.

Nature communications·2025
Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026
Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026
Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026
Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026
Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026
Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Oct 26, 2025

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

3.0K

Neuromorphic computation with a single magnetic domain wall.

Razvan V Ababei1, Matthew O A Ellis2, Ian T Vidamour3

  • 1Department of Material Science and Engineering, University of Sheffield, Sheffield, S1 3JD, UK. r.v.ababei@sheffield.ac.uk.

Scientific Reports
|August 3, 2021
PubMed
Summary
This summary is machine-generated.

Magnetic domain walls in nanowires can perform machine learning tasks. This research demonstrates their potential for efficient, nanoscale neuromorphic computing by modeling their non-linear dynamics.

More Related Videos

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons
09:54

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons

Published on: July 14, 2021

5.0K
Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

9.6K

Related Experiment Videos

Last Updated: Oct 26, 2025

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

3.0K
Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons
09:54

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons

Published on: July 14, 2021

5.0K
Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

9.6K

Area of Science:

  • Physics
  • Materials Science
  • Computer Science

Background:

  • Conventional hardware struggles with machine learning due to architectural mismatches.
  • Neuromorphic computing offers a more efficient alternative by leveraging physical system dynamics.

Purpose of the Study:

  • To demonstrate the feasibility of using magnetic domain wall dynamics for reservoir computing.
  • To explore the potential of nanoscale magnetic devices for efficient machine learning.

Main Methods:

  • Modeling domain wall dynamics in a nickel nanowire using 1D collective coordinates and micromagnetic simulations.
  • Utilizing the non-linear dynamics of domain walls within an engineered potential well.
  • Implementing reservoir computing by modulating magnetic field amplitude for signal injection.

Main Results:

  • Domain wall dynamics were shown to be analogous to the Duffing oscillator.
  • Successful execution of machine learning tasks including signal classification (sine/square waves) and digit recognition (spoken and handwritten).
  • Demonstrated the potential for time-multiplexed signal injection via magnetic field modulation.

Conclusions:

  • Individual magnetic domain walls are suitable for implementing reservoir computing hardware.
  • This work paves the way for nanoscale neuromorphic devices for complex data analysis.
  • Magnetic domain walls offer a promising avenue for efficient, low-power AI hardware.