Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steady Flow of a Fluid Stream

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...

Laminar and Turbulent Flow

Laminar and Turbulent Flow

Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the streamlines...

Turbulent Flow

Turbulent Flow

Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent spots,...

Introduction to Types of Flows

Introduction to Types of Flows

Fluid flows are categorized by dimensionality and behavior, with one-dimensional flow being the simplest form, where properties like velocity and pressure change only along a single axis. Water moving through straight pipes exemplifies this flow type, as variations in other directions are minimal. One-dimensional analysis helps simplify understanding such flows, focusing solely on changes along the pipe's length.
Two-dimensional flow involves changes in both length and height, as seen in air...

Entropy Change in Reversible Processes

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

Entropy

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...

You might also read

Related Articles

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

Sort by

Same author

Identifying the net information flow direction in mutually coupled non-identical chaotic oscillators.

Chaos (Woodbury, N.Y.)·2026

Same author

Estimation of Information Flow-Based Causality with Coarsely Sampled Time Series.

Entropy (Basel, Switzerland)·2026

Same author

Atypical hierarchical brain connectivity in autism: Insights from stepwise causal analysis using Liang information flow.

NeuroImage·2025

Same author

Temporal and spatial variability of large-scale dynamic brain networks in ASD.

European child & adolescent psychiatry·2025

Same author

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024

Same author

The Causal Interaction between Complex Subsystems.

Entropy (Basel, Switzerland)·2022

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

Information flow within stochastic dynamical systems.

¹China Institute for Advanced Study, Beijing, China. san@pacific.harvard.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|October 15, 2008

Summary

We developed a new method to measure information flow in complex systems. This approach quantifies causality, extending beyond traditional correlation analysis for dynamical systems.

Area of Science:

Physics
Dynamical Systems
Information Theory

Background:

Information flow is crucial in physics and dynamical systems.
Existing methods lack quantitative causality measures.
Need for a rigorous formalism for stochastic systems.

Purpose of the Study:

Establish a rigorous formalism for information transfer in generic stochastic dynamical systems.
Derive an explicit formula for the information transfer measure.
Extend causality analysis beyond correlation and mutual information.

Main Methods:

Developed a generic stochastic dynamical system framework.
Derived an explicit formula for information transfer.
Validated the formula using a two-dimensional Langevin equation.

More Related Videos

Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes

Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes

Published on: February 13, 2021

Related Experiment Videos

Last Updated: Jun 29, 2026

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes

Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes

Published on: February 13, 2021

Main Results:

Obtained an explicit formula for information transfer with asymmetry.
Demonstrated formula equivalence to deterministic systems under specific conditions.
Showcased that high correlation does not imply information transfer.
Information flow analysis quantifies causality.

Conclusions:

The proposed formalism provides a rigorous measure of information transfer.
Information flow analysis offers a more nuanced understanding of system dynamics than correlation.
This method advances the quantitative analysis of causality in complex systems.