Search research articles

ACERCA DE JoVE

Visión General Liderazgo Blog Centro de Ayuda JoVE

AUTORES

Proceso de Publicación Consejo Editorial Alcance y Políticas Revisión por Pares Preguntas Frecuentes Enviar

BIBLIOTECARIOS

Testimonios Suscripciones Acceso Recursos Consejo Asesor de Bibliotecas Preguntas Frecuentes

INVESTIGACIÓN

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archivo

EDUCACIÓN

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Centro de Recursos para Profesores Sitio de Profesores

Términos y Condiciones de Uso

Política de Privacidad

Videos de Conceptos Relacionados

Energy Associated With a Charge Distribution

Energy Associated With a Charge Distribution

The work done to bring a charge through a distance r is given by the potential difference between the initial and the final position. To assemble a collection of point charges, the total work done can be expressed in terms of the product of each pair of charges divided by their separation distance, defined with respect to a suitable origin. Solving this expression gives the energy stored in a point charge distribution.

Maxwell-Boltzmann Distribution: Problem Solving

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

Dimensional Analysis

Dimensional Analysis

Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Trends in Lattice Energy: Ion Size and Charge

Trends in Lattice Energy: Ion Size and Charge

An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:

También podría leer

Artículos Relacionados

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

Ordenar por

Same author

Coral restoration alters reef soundscapes but machine learning and manual analyses suggest different recovery rates.

PloS one·2026

Same author

Revisiting low penetrance retinoblastoma: an integrated clinical, genetic, and bioinformatic analysis.

Human molecular genetics·2026

Same author

Measurement of ion acceleration and diffusion in a laser-driven magnetized plasma.

Nature communications·2026

Same author

Enabling accurate chemical modeling of shocked energetic materials using a machine learning interatomic potential.

The Journal of chemical physics·2026

Same author

The moving-beam diffraction geometry: the DIAD application of a diffraction scanning probe.

Journal of applied crystallography·2026

Same author

Observation of quantum effects on radiation reaction in strong fields.

Nature communications·2026

Search research articles

Video Experimental Relacionado

Updated: Nov 5, 2025

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

El futuro basado en datos de la física de alta densidad de energía

Peter W Hatfield¹, Jim A Gaffney², Gemma J Anderson³

¹Clarendon Laboratory, University of Oxford, Parks Road, Oxford, UK. peter.hatfield@physics.ox.ac.uk.

|May 20, 2021

Resumen

El aprendizaje automático está revolucionando la física de alta densidad de energía mediante el análisis de interacciones plasmáticas complejas. Los métodos basados en datos permiten experimentos más rápidos y control automático, avanzando nuestra comprensión de las condiciones extremas.

Más Videos Relacionados

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

Videos de Experimentos Relacionados

Last Updated: Nov 5, 2025

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

Área de la Ciencia:

Física de alta densidad de energía
Física del plasma
La astrofísica
Fusión nuclear

Sus antecedentes:

Las condiciones extremas crean plasmas altamente no lineales y fuertemente acoplados.
Comprender estos plasmas es crucial para la astrofísica, la fusión nuclear y la física fundamental.
Los enfoques teóricos y experimentales tradicionales se enfrentan a desafíos debido a la complejidad del sistema.

Objetivo del estudio:

Explorar el papel transformador del aprendizaje automático (ML) y los métodos basados en datos en la física de alta densidad de energía.
Para resaltar cómo ML puede superar las no linealidades y los fuertes acoplamientos inherentes a los sistemas físicos extremos.
Proponer un camino a seguir para que la comunidad de investigación aproveche estas nuevas herramientas computacionales.

Principales métodos:

Aplicación de modelos de aprendizaje automático para analizar grandes conjuntos de datos de experimentos de alta densidad de energía.
Desarrollo de métodos basados en datos para la interpretación en tiempo real de datos de diagnóstico.
Utilizando ML para el control automático de las instalaciones de física extrema y actualizaciones de modelos de física.

Principales resultados:

Los modelos de aprendizaje automático pueden descubrir rápidamente interacciones complejas dentro de grandes conjuntos de datos, mejorando la comprensión fundamental.
Los avances permiten la interpretación de datos en tiempo real y el control automático de los experimentos.
Este cambio acelera el ritmo de la investigación en la física extrema.

Conclusiones:

El aprendizaje automático y los enfoques basados en datos son esenciales para avanzar en la física de alta densidad de energía.
La comunidad necesita adaptar el diseño de la investigación, la formación y las mejores prácticas para incorporar estos métodos.
La inversión en el diagnóstico sintético y el apoyo al análisis de datos es crucial para el progreso futuro.