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
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

314
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
314
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
State Space to Transfer Function01:21

State Space to Transfer Function

533
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
533
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

371
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
371
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 author

From PINNs to PIKANs: recent advances in physics-informed machine learning.

Machine learning for computational science and engineering·2026
Same author

Automatic selection of the best neural architecture for time series forecasting.

Nature communications·2026
Same author

MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.

Science advances·2026
Same author

Mitochondrial micro-nano reactors via enhancing mitochondrial transfer and mitophagy for alleviating metabolic crisis.

Bioactive materials·2026
Same author

An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.

Lab on a chip·2026
Same author

A Multiscale Signaling-Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease.

bioRxiv : the preprint server for biology·2026

Video Experimental Relacionado

Updated: Jan 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K

Modelos de espacio de estados: operadores neuronales precisos y eficientes para sistemas dinámicos

Zheyuan Hu1, Nazanin Ahmadi Daryakenari2, Qianli Shen1

  • 1Department of Computer Science, National University of Singapore, 119077, Singapore.

Neural networks : the official journal of the International Neural Network Society
|December 25, 2025
PubMed
Resumen

Mamba, un nuevo modelo de espacio de estados, sobresale en el aprendizaje de sistemas dinámicos. Ofrece precisión y eficiencia superiores, especialmente en tareas de extrapolación desafiantes, superando a los métodos existentes en el aprendizaje automático científico.

Palabras clave:
Sistema dinámicoMambaOperador neuronalModelo de espacio de estados

Más Videos Relacionados

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.6K
Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

414

Videos de Experimentos Relacionados

Last Updated: Jan 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.6K
Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

414

Área de la Ciencia:

  • Modelado de Sistemas Dinámicos
  • Aprendizaje Automático Científico
  • Ciencia Computacional

Sus antecedentes:

  • El aprendizaje automático basado en física (PIML) ofrece predicciones más rápidas y generalizables para sistemas dinámicos que los métodos clásicos.
  • Los modelos PIML existentes, como RNN, transformadores y operadores neuronales, luchan con la integración a largo plazo, las dependencias a largo plazo, la dinámica caótica y la extrapolación.
  • Estas limitaciones dificultan la predicción precisa y eficiente de sistemas dinámicos complejos.

Objetivo del estudio:

  • Introducir modelos de espacio de estados implementados en Mamba para el aprendizaje preciso y eficiente de operadores de sistemas dinámicos.
  • Abordar las limitaciones de las arquitecturas actuales para capturar dependencias a largo plazo y la eficiencia computacional.
  • Evaluar el rendimiento de Mamba frente a 11 modelos de referencia en bancos de pruebas de extrapolación estrictos y una aplicación del mundo real.

Principales métodos:

  • Implementación de modelos de espacio de estados utilizando la arquitectura Mamba.
  • Desarrollo de nuevos bancos de pruebas de extrapolación para evaluar rigurosamente la generalización del modelo.
  • Comparación de Mamba frente a 11 modelos de referencia en tareas de interpolación y extrapolación.
  • Aplicación de Mamba a un problema de farmacología de sistemas cuantitativos para la evaluación de la eficacia de los fármacos.

Principales resultados:

  • Mamba demostró un rendimiento superior tanto en interpolación como en tareas de extrapolación desafiantes.
  • Mamba se clasificó consistentemente entre los mejores modelos con el menor costo computacional.
  • El modelo exhibió capacidades de extrapolación excepcionales, superando a los métodos existentes.
  • Mamba mostró un buen rendimiento en una aplicación del mundo real de farmacología de sistemas cuantitativos con datos limitados.

Conclusiones:

  • Mamba presenta una herramienta potente y eficiente para el aprendizaje de operadores de sistemas dinámicos.
  • Su capacidad para capturar dependencias a largo plazo y su eficiencia computacional lo hacen adecuado para tareas complejas de aprendizaje automático científico.
  • Mamba muestra un potencial significativo para avanzar en la investigación en modelado de sistemas dinámicos y farmacología de sistemas cuantitativos.