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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

268
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
268
PD Controller: Design01:26

PD Controller: Design

582
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
582
Typical Model Studies01:30

Typical Model Studies

602
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
602
Gradually Varying Flow01:29

Gradually Varying Flow

376
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
376
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

638
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
638
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

255
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
255

También podría leer

Artículos Relacionados

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

Ordenar por
Same authorSame journal

Efficient reinforcement learning for urban drainage control via a neural network-based model using truncated and parallel rollouts.

Water research·2026
Same author

Physics-informed graph inference and prediction for global state estimation in water distribution networks.

Water research·2026
Same author

A graph-based frequency-domain model enables highly efficient modelling of sewer network hydraulics.

Water research·2026
Same author

Making waves: Model transfer as a key pathway to improving the generalization of machine learning in wastewater treatment engineering.

Water research·2026
Same author

Toward explainable and generalizable data-driven modeling in real wastewater treatment plants: Utilizing bidimensional interpretable deep learning and cross-scenario transfer learning.

Journal of environmental management·2026
Same author

Research on the threshold of the supply and demand of ecosystem services.

PloS one·2026

Video Experimental Relacionado

Updated: Jan 7, 2026

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

915

Modelos basados en redes neuronales diferenciables permiten la optimización basada en gradientes para el control

Zhiyu Zhang1, Wenchong Tian2, Zhenliang Liao3

  • 1School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China; College of Environmental Science and Engineering, Tongji University, 200092, Shanghai, China; State Key Laboratory of Marine Environmental Health, City University of Hong Kong, Hong Kong SAR, China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, China.

Water research
|December 25, 2025
PubMed
Resumen

El control predictivo de modelos (MPC) para redes de drenaje urbano reduce significativamente las inundaciones. Este nuevo marco utiliza redes neuronales para una optimización más rápida y en tiempo real, superando a los métodos tradicionales.

Palabras clave:
optimización basada en gradientescontrol predictivo de modelosred neuronalcontrol en tiempo realred de drenaje urbano

Videos de Experimentos Relacionados

Last Updated: Jan 7, 2026

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure
07:15

Parameterizing V-notch Weir Equations for Flow Monitoring in a Drainage Control Structure

Published on: April 25, 2025

915

Sus antecedentes:

  • Las redes de drenaje urbano enfrentan desafíos de desbordamiento e inundación debido a la capacidad hidráulica limitada.
  • El control predictivo de modelos (MPC) ofrece potencial de optimización pero es computacionalmente intensivo.
  • Los métodos actuales de MPC se basan en modelos físicos, lo que dificulta la aplicación en tiempo real.

Conclusiones:

  • El marco MPC desarrollado mejora significativamente la eficiencia computacional para la optimización de redes de drenaje urbano.
  • Las redes neuronales diferenciables integradas con la optimización basada en gradientes permiten capacidades de control en tiempo real.
  • Este enfoque ofrece una solución práctica para mitigar las inundaciones urbanas y mejorar la utilización de la capacidad hidráulica.