Jove
Visualize
Contact Us
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 Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

171
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...
171
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

241
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
241
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

197
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,...
197
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

146
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
146
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

255
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...
255
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

354
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,...
354

You might also read

Related Articles

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

Sort by
Same journal

Stochastic Poincaré maps for a slow-fast system with white noises: Approximation and visualization.

Chaos (Woodbury, N.Y.)·2026
Same journalSame Topic

On a stable torus in a 3D system with a saddle-focus.

Chaos (Woodbury, N.Y.)·2026
Same journal

Targeted interventions suppress epidemic outbreaks in spatial higher-order activity-driven networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Erratum: "Hierarchical organization of bursty trains in event sequences" [Chaos 35, 113115 (2025)].

Chaos (Woodbury, N.Y.)·2026
Same journal

Deterministic control of CW/CCW alternation by dual-frequency injection in a heterogeneous oscillator ring.

Chaos (Woodbury, N.Y.)·2026
Same journalSame Topic

A CTRW-driven subdiffusive fractional Brownian bridge in the reconstruction of missing experimental data.

Chaos (Woodbury, N.Y.)·2026

Related Experiment Video

Updated: Nov 15, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

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

Published on: December 4, 2017

8.8K

Using machine-learning modeling to understand macroscopic dynamics in a system of coupled maps.

Francesco Borra1, Marco Baldovin1

  • 1Dipartimento di Fisica, Sapienza Università di Roma, p.le A. Moro 5, I-00185 Rome, Italy.

Chaos (Woodbury, N.Y.)
|March 3, 2021
PubMed
Summary

Machine learning models can uncover hidden physics in complex systems. This study uses machine learning to analyze the macroscopic dynamics of coupled maps, revealing insights into their behavior.

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.4K
Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.8K

Related Experiment Videos

Last Updated: Nov 15, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

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

Published on: December 4, 2017

8.8K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.4K
Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

2.8K

Area of Science:

  • Complex Systems
  • Statistical Physics
  • Machine Learning

Background:

  • Machine learning offers powerful tools for modeling dynamical systems.
  • These techniques can also serve as investigative instruments for underlying physics.
  • Analyzing macroscopic motion from complex systems often requires advanced methods.

Purpose of the Study:

  • To test the capability of stochastic machine learning in describing complex evolution laws.
  • To gain insights into the physics governing macroscopic dynamics.
  • To compare machine learning-derived models with direct numerical computations.

Main Methods:

  • A coarse-grained Markov process was developed for macroscopic dynamics.
  • Both a machine learning approach and direct numerical computation were used.
  • The information provided to the machine learning network was modulated.

Main Results:

  • The machine learning approach successfully described nontrivial evolution laws.
  • Analysis revealed the effective dimension of the attractor.
  • Memory effects and multiscale structures within the dynamics were identified.

Conclusions:

  • Machine learning is effective for investigating the physics of macroscopic dynamics.
  • The study demonstrates the utility of machine learning beyond mere data modeling.
  • This approach provides deeper insights into complex system behaviors.