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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

347
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,...
347
Modeling with Differential Equations01:25

Modeling with Differential Equations

20
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
20
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

292
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...
292
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

502
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,...
502
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

394
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...
394

You might also read

Related Articles

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

Sort by
Same author

Evolution's hidden architecture: a non-lipschitz theory of creation and catastrophe.

BMC ecology and evolution·2025
Same author

Forecasting with an N-dimensional Langevin equation and a neural-ordinary differential equation.

Chaos (Woodbury, N.Y.)·2024
Same author

Physics-informed Bayesian inference of external potentials in classical density-functional theory.

The Journal of chemical physics·2023
Same author

Machine Learning Memory Kernels as Closure for Non-Markovian Stochastic Processes.

IEEE transactions on neural networks and learning systems·2022
Same author

Physics-constrained Bayesian inference of state functions in classical density-functional theory.

The Journal of chemical physics·2022
Same author

Understanding Soaring Coronavirus Cases and the Effect of Contagion Policies in the UK.

Vaccines·2021

Related Experiment Video

Updated: Jan 17, 2026

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

9.0K

Data-driven reconstruction of a multivariate Langevin equation to model complex systems.

Antonio Malpica-Morales1, Miguel A Durán-Olivencia1,2, Serafim Kalliadasis1

  • 1Imperial College, Department of Chemical Engineering, London SW7 2AZ, United Kingdom.

Physical Review. E
|September 16, 2025
PubMed
Summary

This study introduces a data-driven multivariate Langevin equation (LE) to model complex systems. The method accurately captures system dynamics without prior knowledge, proving effective in mechanics and financial markets.

More Related Videos

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

502
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.9K

Related Experiment Videos

Last Updated: Jan 17, 2026

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

9.0K
Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

502
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.9K

Area of Science:

  • Complex Systems Analysis
  • Statistical Mechanics
  • Quantitative Finance

Background:

  • Modeling complex systems with intricate interactions is challenging.
  • Existing methods often require a priori knowledge of underlying mechanisms.
  • Accurate description of system observables is crucial for understanding behavior.

Purpose of the Study:

  • To propose a data-driven multivariate Langevin equation (LE) for approximating complex system observables.
  • To unravel key features of complex systems without requiring prior knowledge.
  • To demonstrate the framework's adaptability and reliability across diverse applications.

Main Methods:

  • Reconstruction of drift and diffusion terms in the LE using a nonparametric technique.
  • Application of Kramers-Moyal coefficients for LE term identification.
  • Benchmarking with a mechanical system (particle in a bistable potential) and financial data (electricity prices, currency exchange rates).

Main Results:

  • The framework accurately identifies equilibrium values, metastability regions, and distinct diffusion behaviors.
  • Successful application to financial markets (electricity day-ahead prices, currency-exchange rates) where LE has not been previously used.
  • Demonstrated functional-agnostic approach, contrasting with domain-specific price-equation models.

Conclusions:

  • The proposed nonparametric multivariate LE framework offers a reliable, data-driven approach to modeling complex systems.
  • The method effectively extracts pertinent information and system features without necessitating a priori domain knowledge.
  • This approach provides a powerful tool for analyzing diverse complex systems, including financial markets.