Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
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

A data-driven modeling study on the accurate identification of Doppler-free saturated absorption spectra in diatomic tellurium (130Te2).

The Journal of chemical physics·2026
Same journal

Anharmonic phonons via quantum thermal bath simulations.

The Journal of chemical physics·2026
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
Ver todos los artículos relacionados

Video Experimental Relacionado

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

Representación estocástica de funciones de onda basadas en redes neuronales en evolución temporal

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
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo método computacional que combina la representación estocástica y las redes neuronales para resolver la ecuación de Schrödinger dependiente del tiempo (TDSE) para la dinámica de electrones. El enfoque modela con precisión los procesos de ionización en campos láser intensos.

Palabras clave:
dinámica de electronesecuación de Schrödinger dependiente del tiemporedes neuronalesrepresentación estocásticaprocesos de ionizacióncampos láser intensosmecánica cuánticafísica computacionalfísica de los átomosespectroscopia ultrarrápidainteracciones láser-materia

Más Videos Relacionados

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

Videos de Experimentos Relacionados

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

Área de la Ciencia:

  • Mecánica cuántica
  • Física computacional
  • Física de los átomos

Sus antecedentes:

  • Resolver la ecuación de Schrödinger dependiente del tiempo (TDSE) es crucial para comprender la dinámica electrónica en la espectroscopia ultrarrápida y las interacciones láser-materia.
  • Las soluciones exactas de la TDSE son computacionalmente costosas debido al crecimiento exponencial del espacio de Hilbert con la dimensionalidad del sistema.

Objetivo del estudio:

  • Desarrollar y validar un método computacionalmente eficiente para resolver la TDSE.
  • Modelar la dinámica electrónica no adiabática, específicamente los procesos de ionización bajo campos láser intensos.

Principales métodos:

  • Integración del marco de representación estocástica con un ansaño de función de onda de red neuronal.
  • Validación en sistemas unidimensionales de un solo electrón que simulan la dinámica de ionización.
  • Exploración de la extensión a sistemas tridimensionales.

Principales resultados:

  • Reproducción precisa de la evolución cuántica, incluida la evolución de la energía y el dipolo durante la ionización.
  • Demostró la viabilidad de aplicar el método a sistemas tridimensionales.
  • Identificó la necesidad de estrategias de estabilización avanzadas para simulaciones de mayor dimensionalidad.

Conclusiones:

  • El enfoque híbrido propuesto ofrece una vía prometedora para simular dinámicas cuánticas complejas.
  • El método muestra potencial para la modelización precisa de la dinámica electrónica ultrarrápida en sistemas realistas.
  • Se requiere un mayor desarrollo para una aplicación robusta a problemas de mayor dimensionalidad.