Jove
Visualize
Contact Us

Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

110
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,...
110
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
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...
88

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Microelectromechanical System Resonant Devices: A Guide for Design, Modeling and Testing.

Micromachines·2025
Same author

Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks.

Chaos (Woodbury, N.Y.)·2023
Same author

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation.

International journal for numerical methods in biomedical engineering·2023
Same author

Learning high-order interactions for polygenic risk prediction.

PloS one·2023
Same author

SUIHTER: a new mathematical model for COVID-19. Application to the analysis of the second epidemic outbreak in Italy.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same author

An Autoencoder-Based Deep Learning Approach for Load Identification in Structural Dynamics.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Aug 5, 2025

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication
10:16

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Published on: December 2, 2011

14.1K

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches.

Giorgio Gobat1, Stefania Fresca2, Andrea Manzoni2

  • 1Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning creates accurate reduced-order models for micro-electro-mechanical-systems (MEMS). This enables efficient real-time simulation and optimization of complex MEMS devices like gyroscopes.

Keywords:
data-driven modeldeep learninginvariant manifoldsnonlinear dynamicsreduced order modeling

More Related Videos

A Polymer-based Piezoelectric Vibration Energy Harvester with a 3D Meshed-Core Structure
09:51

A Polymer-based Piezoelectric Vibration Energy Harvester with a 3D Meshed-Core Structure

Published on: February 20, 2019

25.5K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K

Related Experiment Videos

Last Updated: Aug 5, 2025

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication
10:16

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Published on: December 2, 2011

14.1K
A Polymer-based Piezoelectric Vibration Energy Harvester with a 3D Meshed-Core Structure
09:51

A Polymer-based Piezoelectric Vibration Energy Harvester with a 3D Meshed-Core Structure

Published on: February 20, 2019

25.5K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K

Area of Science:

  • Engineering
  • Computational Science
  • Physics

Background:

  • Micro-electro-mechanical-systems (MEMS) are vital sensors and actuators with complex nonlinear behaviors.
  • Full-order models of MEMS are computationally intensive, limiting real-time simulation and optimization.

Purpose of the Study:

  • To develop accurate and efficient reduced-order models (ROMs) for MEMS using deep learning.
  • To enable real-time simulation and optimization of complex systems incorporating MEMS.

Main Methods:

  • Application of deep learning techniques to full-order MEMS representations.
  • Testing and validation on diverse MEMS structures like micromirrors, arches, and gyroscopes.
  • Comparison with direct parametrization for nonlinear normal mode extraction.

Main Results:

  • Deep learning successfully generated accurate, real-time ROMs for MEMS.
  • The models replicated invariant manifolds and converged to nonlinear normal modes.
  • The approach demonstrated generalization to complex electromechanical multiphysics problems.

Conclusions:

  • Deep learning offers a powerful, non-intrusive method for creating efficient MEMS simulations.
  • This technique facilitates the optimization of complex systems involving MEMS devices.
  • The approach is versatile and applicable to various MEMS designs and multiphysics scenarios.