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

Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

3.4K
In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
3.4K
Nonconscious Mimicry01:13

Nonconscious Mimicry

5.2K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
5.2K
Rapidly Varying Flow01:24

Rapidly Varying Flow

573
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
573

You might also read

Related Articles

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

Sort by
Same author

Optimized mechano-fluidic metamaterials inspired by deep-sea sponges.

Nature communications·2026
Same author

Delamination and out-of-plane deformation in drying colloidal suspensions.

Soft matter·2026
Same author

Advancing regulatory variant effect prediction with AlphaGenome.

Nature·2026
Same author

Ising energy model for the stochastic prediction of tumor islets.

ArXiv·2025
Same author

Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss.

Nature communications·2025
Same author

Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone.

PloS one·2025
Same journal

Tuna-Like Swimmers Experience a Fluid-Mediated Stable Side-by-Side Formation.

Bioinspiration & biomimetics·2026
Same journal

Analysis of Inter-leg Coordination Mechanisms in Cricket Locomotion: Insights from Thoracic Ganglion Network Transection.

Bioinspiration & biomimetics·2026
Same journal

Controllability and Trajectory Controls of an Underactuated Flapping-Wing Aircraft Steered by Center of Gravity.

Bioinspiration & biomimetics·2026
Same journal

Leveling the head relative to ground slope improves visual odometry in simulated bees.

Bioinspiration & biomimetics·2026
Same journal

Study on Aerodynamic Performance of a Bio-inspired Flapping Wing under the Effect of Anti-reversal Duration.

Bioinspiration & biomimetics·2026
Same journal

A Physics-Guided Neural Network Framework for Prediction and Control of Spring-Mass Running.

Bioinspiration & biomimetics·2026
See all related articles

Related Experiment Video

Updated: Mar 5, 2026

A Rapidly Incremented Tethered-Swimming Maximal Protocol for Cardiorespiratory Assessment of Swimmers
09:24

A Rapidly Incremented Tethered-Swimming Maximal Protocol for Cardiorespiratory Assessment of Swimmers

Published on: January 28, 2020

9.4K

Synchronisation through learning for two self-propelled swimmers.

Guido Novati1, Siddhartha Verma, Dmitry Alexeev

  • 1Computational Science and Engineering Laboratory, Clausiusstrasse 33, ETH Zürich, CH-8092, Switzerland. Wallace Visiting Professor, Massachusetts Institute of Technology, MA, United States of America.

Bioinspiration & Biomimetics
|March 30, 2017
PubMed
Summary
This summary is machine-generated.

Fish schooling relies on coordinated swimming. Simulations show learning algorithms help fish synchronize movements, reducing energy use and improving efficiency by adapting to hydrodynamic interactions.

More Related Videos

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

13.1K
Swimming Performance Assessment in Fishes
05:12

Swimming Performance Assessment in Fishes

Published on: May 20, 2011

26.0K

Related Experiment Videos

Last Updated: Mar 5, 2026

A Rapidly Incremented Tethered-Swimming Maximal Protocol for Cardiorespiratory Assessment of Swimmers
09:24

A Rapidly Incremented Tethered-Swimming Maximal Protocol for Cardiorespiratory Assessment of Swimmers

Published on: January 28, 2020

9.4K
Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

13.1K
Swimming Performance Assessment in Fishes
05:12

Swimming Performance Assessment in Fishes

Published on: May 20, 2011

26.0K

Area of Science:

  • Fluid dynamics
  • Biophysics
  • Robotics

Background:

  • Coordinated motion is key in fish schooling.
  • Hydrodynamic interactions between swimmers are complex and non-linear.
  • Understanding how swimmers adapt to these interactions is crucial.

Purpose of the Study:

  • To investigate how self-propelled swimmers can synchronize their motion.
  • To determine if learned motion patterns are superior to pre-specified ones for synchronization.
  • To quantify the energy savings and efficiency gains from synchronized swimming.

Main Methods:

  • Simulations of two self-propelled, fish-like bodies.
  • Comparison of pre-specified motion patterns versus learned motion patterns.
  • Application of reinforcement learning for motion adaptation.

Main Results:

  • Pre-specified motions in a leader-follower setup did not sustain synchronization.
  • Learned motion patterns allowed a follower to synchronize with a leader.
  • Synchronized swimming reduced follower energy expenditure by up to [Formula: see text] and increased efficiency by [Formula: see text].

Conclusions:

  • Swimmers can adapt motion patterns to compensate for hydrodynamic interactions.
  • Learned synchronization enables energetically beneficial coordinated swimming.
  • Exploiting flow structures enhances efficiency in synchronized groups.