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

State Space Representation01:27

State Space Representation

496
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
496
Graphing the Wave Function01:13

Graphing the Wave Function

2.8K
Consider the wave equation for a sinusoidal wave moving in the positive x-direction. The wave equation is a function of both position and time. From the wave equation, two different graphs can be plotted.
2.8K
Propagation of Action Potentials01:23

Propagation of Action Potentials

8.7K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
8.7K
Exponential and Sinusoidal Signals01:18

Exponential and Sinusoidal Signals

663
The exponential function is crucial for characterizing waveforms that rise and decay rapidly. This continuous-time exponential function is defined using exponential terms with constants α and A. When both constants are real, the function is represented as,
663
Transfer Function to State Space01:23

Transfer Function to State Space

727
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
727
Equations of Wave Motion01:02

Equations of Wave Motion

8.2K
Mathematically, the motion of a wave can be studied using a wavefunction. Consider a string oscillating up and down in simple harmonic motion, having a period T. The wave on the string is sinusoidal and is translated in the positive x-direction as time progresses. Sine is a function of the angle θ, oscillating between +A and −A and repeating every 2π radians. To construct a wave model, the ratio of the angle θ and the position x is considered.
8.2K

You might also read

Related Articles

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

Sort by
Same author

Dataset distillation for machine learning force field in phase transition regime.

The Journal of chemical physics·2026
Same author

Deep-learning electronic structure calculations.

Nature computational science·2025
Same author

Down to one network for computing crystalline materials.

Nature computational science·2025
Same author

A multi-resolution systematically improvable quantum embedding scheme for large-scale surface chemistry calculations.

Nature communications·2025
Same author

Individual and Cooperative Superexchange Enhancement in Cuprates.

Journal of chemical theory and computation·2025
Same author

Molecularly resolved mapping of heterogeneous ice nucleation and crystallization pathways using in-situ cryo-TEM.

Nature communications·2025
Same journal

Quantum simulation of alignment dependent differential cross sections in co-propagating molecular beams at cold collision energies.

The Journal of chemical physics·2026
Same journal

Non-additive ion effects on the coil-globule equilibrium of a generic polymer in aqueous salt solutions.

The Journal of chemical physics·2026
Same journal

Insights into the unexpected small reduction of the temperature of maximum density of water by lithium chloride addition.

The Journal of chemical physics·2026
Same journal

Optical frequency comb double-resonance spectroscopy of the 9030-9175 cm-1 states of ethylene.

The Journal of chemical physics·2026
Same journal

Time reversal breaking of colloidal particles in cells.

The Journal of chemical physics·2026
Same journal

Photodynamics of amino acids under UV excitation: Extraterrestrial amino acids.

The Journal of chemical physics·2026
See all related articles

Related Experiment Video

Updated: Jan 8, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K

Stochastic representation of time-evolving neural network-based wavefunctions.

Bizi Huang1, Weizhong Fu1, Ji Chen1,2,3

  • 1School of Physics, Peking University, Beijing 100871, People's Republic of China.

The Journal of Chemical Physics
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method combining stochastic representation and neural networks to solve the time-dependent Schrödinger equation (TDSE) for electron dynamics. The approach accurately models ionization processes in intense laser fields.

More Related Videos

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.3K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.8K

Related Experiment Videos

Last Updated: Jan 8, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.3K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.8K

Area of Science:

  • Quantum mechanics
  • Computational physics
  • Attosecond physics

Background:

  • Solving the time-dependent Schrödinger equation (TDSE) is crucial for understanding electron dynamics in ultrafast spectroscopy and laser-matter interactions.
  • Exact TDSE solutions are computationally expensive due to the exponential growth of Hilbert space with system dimensionality.

Purpose of the Study:

  • To develop and validate a computationally efficient method for solving the TDSE.
  • To model nonadiabatic electron dynamics, specifically ionization processes under intense laser fields.

Main Methods:

  • Integration of the stochastic representation framework with a neural network wavefunction ansatz.
  • Validation on one-dimensional, single-electron systems simulating ionization dynamics.
  • Exploration of extension to three-dimensional systems.

Main Results:

  • Accurate reproduction of quantum evolution, including energy and dipole evolution during ionization.
  • Demonstrated feasibility of applying the method to three-dimensional systems.
  • Identified need for advanced stabilization strategies for higher-dimensional simulations.

Conclusions:

  • The proposed hybrid approach offers a promising avenue for simulating complex quantum dynamics.
  • The method shows potential for accurate modeling of ultrafast electron dynamics in realistic systems.
  • Further development is required for robust application to higher-dimensional problems.