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

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

506
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
506
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
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,...
124
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.6K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.6K
Linear time-invariant Systems01:23

Linear time-invariant Systems

395
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
395
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
121
Second Order systems II01:18

Second Order systems II

167
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
167

You might also read

Related Articles

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

Sort by
Same author

Explaining complex dynamical systems using conditional SHAP analysis with application to multi-variant epidemic dynamics.

Scientific reports·2026
Same author

Leveraging Systems-of-Systems Analysis to Strengthen Epidemic Intelligence for Preparedness and Response.

Health security·2025
Same author

Mathematical expansion and clinical application of chronic kidney disease stage as vector field.

PloS one·2024
Same author

New marker for chronic kidney disease progression and mortality in medical-word virtual space.

Scientific reports·2024
Same author

Practical guide to using Kendall's <i>Ï„</i> in the context of forecasting critical transitions.

Royal Society open science·2022
Same author

Deep learning for centre manifold reduction and stability analysis in nonlinear systems.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2022

Related Experiment Video

Updated: Sep 7, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K

Data-driven prediction in dynamical systems: recent developments.

Amin Ghadami1, Bogdan I Epureanu1

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

New data-driven and AI methods enhance the prediction of complex dynamical systems. These approaches integrate machine learning with dynamical systems theory to overcome challenges in modeling and analysis.

Keywords:
data-driven predictiondynamical systemsmodel discovery

More Related Videos

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.3K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.2K

Related Experiment Videos

Last Updated: Sep 7, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.3K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.2K

Area of Science:

  • Applied Sciences
  • Complex Systems Analysis
  • Dynamical Systems Theory

Background:

  • Societal challenges increasingly involve complex, large-scale systems.
  • Predicting the dynamics of these systems is hindered by high dimensionality and chaotic behavior.
  • Traditional methods struggle with the complexity inherent in modern scientific and engineering problems.

Purpose of the Study:

  • To highlight recent advancements in data-driven and AI-based discovery of dynamical systems.
  • To explore the integration of machine learning with dynamical systems theory.
  • To address the need for improved techniques applicable to a wider range of complex systems.

Main Methods:

  • Leveraging recent advances in data-driven techniques and machine learning.
  • Integrating machine learning approaches with dynamical systems theory.
  • Focusing on data-driven, data-assisted, and artificial intelligence-based discovery methods.

Main Results:

  • Data-driven techniques have revolutionized the modeling and analysis of complex systems.
  • Integration with dynamical systems theory opens new avenues for prediction.
  • Recent developments offer improved applicability to diverse scientific and engineering challenges.

Conclusions:

  • New techniques are crucial for advancing the prediction of complex dynamical systems.
  • The synergy between data-driven methods and dynamical systems theory is key.
  • Continued development is needed to tackle previously unattainable challenges in modeling and prediction.