Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Convolution Properties II01:17

Convolution Properties II

583
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
583
Convolution Properties I01:20

Convolution Properties I

564
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
564
Ogive Graph01:07

Ogive Graph

6.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.6K
Graphing Antiderivatives01:30

Graphing Antiderivatives

48
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
48
Bar Graph01:07

Bar Graph

21.4K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
21.4K
Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

On the Prospect of Chemically Transferable Coarse-Grained Electronic Models for Soft Materials.

The journal of physical chemistry. B·2026
Same author

Molecular Charge Topologies Govern Polar Nematic Ordering.

Journal of the American Chemical Society·2025
Same author

Conformation-Mediated Doping in P3HT:F4TCNQ Dimers from Density Functional Theory.

The journal of physical chemistry. A·2025
Same author

All-Atom Reactive Monte Carlo Molecular Dynamics for Molecular Doping in Organic Semiconductors.

Journal of chemical theory and computation·2025
Same author

Ubiquitous Chiral Symmetry Breaking of Conjugated Polymers via Liquid-Liquid Phase Separation.

Journal of the American Chemical Society·2025
Same author

Functional monomer design for synthetically accessible polymers.

Chemical science·2025
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
Same journal

Time reversal breaking of colloidal particles in cells.

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

Video Experimental Relacionado

Updated: Jan 23, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

Propagadores electrónicos convolucionales de grafos basados en aprendizaje automático

Annabella E DeBernardo1,2, Nicholas E Jackson1,2

  • 1Department of Chemistry, University of Illinois, Urbana, Illinois 61801, USA.

The Journal of chemical physics
|January 22, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Desarrollamos un marco de aprendizaje automático de grafos para simular la dinámica electrónica cuántica. Nuestros modelos predicen con precisión la evolución de la función de onda y la densidad electrónica, lo que permite simulaciones cuánticas escalables.

Palabras clave:
aprendizaje automáticodinámica cuánticaredes neuronales de grafospropagación de la función de ondadensidad electrónicasimulaciones escalables

Más Videos Relacionados

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

943
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

Videos de Experimentos Relacionados

Last Updated: Jan 23, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

943
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

Área de la Ciencia:

  • Mecánica cuántica
  • Química computacional
  • Aprendizaje automático

Sus antecedentes:

  • La simulación de la evolución temporal de sistemas cuánticos requiere muchos recursos computacionales.
  • Los métodos existentes tienen problemas de escalabilidad para sistemas moleculares y de fase condensada complejos.

Objetivo del estudio:

  • Desarrollar un nuevo marco de aprendizaje automático basado en grafos para simular la dinámica electrónica.
  • Introducir y evaluar dos variantes del modelo: una para funciones de onda y otra para densidades electrónicas.

Principales métodos:

  • Se utilizó una arquitectura recursiva de redes neuronales de grafos de Chebyshev.
  • Se entrenaron modelos con datos de trayectoria de sistemas de enlace fuerte y acoplados electrón-fonón.
  • Se investigó la propagación de funciones de onda complejas y de densidad electrónica.

Principales resultados:

  • Los modelos basados en funciones de onda lograron una propagación a largo plazo casi exacta en varios regímenes.
  • Los modelos solo de densidad mostraron un fuerte rendimiento con funciones de pérdida informadas por la física.
  • Se demostró el potencial para la simulación de la dinámica electrónica independiente de la resolución.

Conclusiones:

  • El marco basado en grafos proporciona una base para simulaciones cuánticas escalables.
  • Este enfoque abre nuevas vías para el estudio de sistemas cuánticos complejos.
  • Los modelos desarrollados ofrecen una simulación eficiente y precisa de procesos electrónicos.