Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Modeling and Similitude01:12

Modeling and Similitude

573
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
573
Morphogenesis02:19

Morphogenesis

30.1K
Plant morphogenesis—the development of a plant’s form and structure—involves several overlapping developmental processes, including growth and cell differentiation. Precursor cells differentiate into specific cell types, which are organized into the tissues and organ systems that make up the functional plant.
30.1K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.3K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.3K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.2K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.2K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
6.5K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

472
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
472

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

A Longitudinal Comprehensive Biospecimen and Clinical Data Repository for Cancer Early Detection: The InAdvance Study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Relationship between the distribution of LEDGF along genes and positions of HIV-1 DNA integration.

mBio·2026
Same author

CShaperApp: Segmenting and analyzing cellular morphologies of the developing <i>Caenorhabditis elegans</i> embryo.

Quantitative biology (Beijing, China)·2026
Same author

An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis.

Quantitative biology (Beijing, China)·2026
Same author

EmbSAM: cell boundary localization and Segment Anything Model for fast images of developing embryos.

Communications biology·2025
Same author

An efficient solution to Hidden Markov Models on trees with coupled branches.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society·2025
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·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 7, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

DiffeoMorph: Aprendizaje para Mofar Formas 3D Mediante Simulaciones Diferenciables Basadas en Agentes

Seong Ho Pahng, Guoye Guan, Benjamin Fefferman

    ArXiv
    |December 25, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    DiffeoMorph permite a los agentes formar colectivamente formas 3D complejas utilizando un novedoso marco diferenciable. Este enfoque avanza en biología del desarrollo, robótica y aprendizaje multiagente al aprender protocolos de morfogénesis.

    Palabras clave:
    morfogénesisaprendizaje profundoredes neuronalesbiología computacionalrobóticaaprendizaje multiagentesimulación diferenciablepolinomios de Zernikeformación de formassistemas autoorganizados

    Más Videos Relacionados

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.6K
    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.6K

    Videos de Experimentos Relacionados

    Last Updated: Jan 7, 2026

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.2K
    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.6K
    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.6K

    Área de la Ciencia:

    • Biología Computacional
    • Robótica
    • Inteligencia Artificial

    Sus antecedentes:

    • Los sistemas biológicos exhiben estructuras 3D complejas formadas por el comportamiento colectivo de los agentes sin control central.
    • La comprensión del control distribuido en la morfogénesis es crucial para la biología del desarrollo, la robótica y el aprendizaje multiagente.

    Objetivo del estudio:

    • Introducir DiffeoMorph, un marco diferenciable para aprender protocolos de morfogénesis.
    • Permitir que una población de agentes forme colectivamente una forma 3D objetivo.

    Principales métodos:

    • Utilizar una red neuronal gráfica equivariante SE(3) basada en atención para las actualizaciones de posición y estado del agente.
    • Emplear una novedosa pérdida de coincidencia de formas basada en polinomios de Zernike 3D para la comparación continua de formas.
    • Implementar un paso de alineación con diferenciación implícita para la invarianza SO(3).

    Principales resultados:

    • Demostrar la superioridad de la pérdida del polinomio de Zernike 3D sobre las métricas estándar.
    • Mostrar la capacidad de DiffeoMorph para generar formas 3D diversas, desde morfologías simples a complejas.
    • Validar la efectividad del marco utilizando señales espaciales mínimas.

    Conclusiones:

    • DiffeoMorph proporciona un marco diferenciable efectivo de extremo a extremo para aprender la formación colectiva de formas.
    • La pérdida de coincidencia de formas desarrollada y los métodos de cálculo de gradientes son robustos y eficientes.
    • Este trabajo ofrece un enfoque prometedor para diseñar sistemas autoorganizados en biología e inteligencia artificial.