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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

88
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
88
Damped Oscillations01:07

Damped Oscillations

5.7K
In the real world, oscillations seldom follow true simple harmonic motion. A system that continues its motion indefinitely without losing its amplitude is termed undamped. However, friction of some sort usually dampens the motion, so it fades away or needs more force to continue. For example, a guitar string stops oscillating a few seconds after being plucked. Similarly, one must continually push a swing to keep a child swinging on a playground.
Although friction and other non-conservative...
5.7K
Oscillations about an Equilibrium Position01:04

Oscillations about an Equilibrium Position

5.4K
Stability is an important concept in oscillation. If an equilibrium point is stable, a slight disturbance of an object that is initially at the stable equilibrium point will cause the object to oscillate around that point. For an unstable equilibrium point, if the object is disturbed slightly, it will not return to the equilibrium point. There are three conditions for equilibrium points—stable, unstable, and half-stable. A half-stable equilibrium point is also unstable, but is named so...
5.4K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

965
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
965
Linear time-invariant Systems01:23

Linear time-invariant Systems

242
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
242

You might also read

Related Articles

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

Sort by
Same author

Shaping chaos in bilayer graphene cavities.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.

Machine learning: science and technology·2025
Same author

Ultracold Molecular Collisions: Quasiclassical, Semiclassical, and Classical Approaches in the Quantum Regime.

Chemical reviews·2025
Same author

Polaron catastrophe within quantum acoustics.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Direct visualization of relativistic quantum scars in graphene quantum dots.

Nature·2024
Same author

Quantum acoustics unravels Planckian resistivity.

Proceedings of the National Academy of Sciences of the United States of America·2024

Related Experiment Video

Updated: Jun 19, 2025

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light
09:19

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

11.4K

Rise and Fall of Anderson Localization by Lattice Vibrations: A Time-Dependent Machine Learning Approach.

Yoel Zimmermann1,2, Joonas Keski-Rahkonen2,3, Anton M Graf3,4

  • 1Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland.

Entropy (Basel, Switzerland)
|July 26, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning categorizes electron-lattice interactions, revealing transient localization. This phenomenon, where electrons are temporarily trapped by lattice vibrations, offers insights into strange metals and material design.

Keywords:
Anderson localizationcoherent statesdynamical disorderlattice vibrationsmachine learningtransient localization

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.0K
Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

10.7K

Related Experiment Videos

Last Updated: Jun 19, 2025

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light
09:19

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

11.4K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.0K
Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

10.7K

Area of Science:

  • Condensed Matter Physics
  • Quantum Acoustics
  • Materials Science

Background:

  • The Fröhlich model describes electron-lattice coupling, but conventional methods like perturbation theory limit exploration.
  • Quantum acoustics reveals the wave nature of lattice vibrations, enabling new studies of electron-lattice interactions.

Purpose of the Study:

  • To apply machine learning for categorizing electron-lattice interaction regimes.
  • To identify and explain novel electron dynamics, specifically transient localization, within the Fröhlich model.

Main Methods:

  • Utilized machine learning algorithms to analyze electron-lattice dynamics.
  • Investigated electron wavepacket behavior under strong lattice vibrations.
  • Employed quantum acoustics to probe electron-lattice interactions beyond perturbation theory.

Main Results:

  • Successfully categorized diverse interaction regimes using machine learning.
  • Identified transient localization as a key dynamic, characterized by temporary Anderson localization of electronic wavepackets.
  • Demonstrated that lattice evolution releases trapped electronic wavepackets.

Conclusions:

  • Machine learning effectively illuminates complex electron-lattice dynamics, including transient localization.
  • Transient localization may explain phenomena in strange metals, offering new avenues for research.
  • Time-dependent machine learning can guide the design of materials with specific electron-lattice properties.