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

Conservation of Energy: Application01:12

Conservation of Energy: Application

6.4K
When solving problems using the energy conservation law, the object (system) to be studied should first be identified. Often, in applications of energy conservation, we study more than one body at the same time. Second, identify all forces acting on the object and determine whether each force doing work is conservative. If a non-conservative force (e.g., friction) is doing work, then mechanical energy is not conserved. The system must then be analyzed with non-conservative work. Third, for...
6.4K
Conservation of Energy00:54

Conservation of Energy

8.7K
The terms 'conserved quantity' and 'conservation law' have specific scientific meanings in physics, which differ from the meanings associated with their everyday use. For example, in everyday usage, water could be conserved by not using it, by using less of it, or by re-using it. However, in scientific terms, a conserved quantity of a system stays constant, changes by a definite amount that is transferred to other systems, and is converted into other forms of that...
8.7K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

59
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,...
59
Classification of Systems-I01:26

Classification of Systems-I

164
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
164
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

452
Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
For steady flow systems, the time derivative of the stored energy becomes zero since there is no energy accumulation within the control volume. This simplifies the energy equation to:
452
Conservation of Mass in Finite Cotrol Volume01:16

Conservation of Mass in Finite Cotrol Volume

896
The principle of conservation of mass is a fundamental law in fluid mechanics and is applied using the continuity equation. We apply the concept to a finite control volume to derive the continuity equation.
A system is defined as a collection of unchanging contents, and the conservation of mass states that a system's mass is constant.
896

You might also read

Related Articles

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

Sort by
Same author

Efficient Calculation of Electrostatic Energies for Large-Scale Nonadiabatic Molecular Dynamics in a Site Basis.

Journal of chemical theory and computation·2025
Same author

Representative Random Sampling of Chemical Space.

Journal of chemical theory and computation·2025
Same author

Intrinsic dimensionality of molecular properties.

The Journal of chemical physics·2025
Same author

Hammett-Inspired Product Baseline for Data-Efficient Δ-ML in Chemical Space.

Journal of chemical theory and computation·2025
Same author

Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br.

Scientific data·2025
Same author

Transferability of atomic energies from alchemical decomposition.

The Journal of chemical physics·2024
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: May 21, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K

Machine learning conservation laws of dynamical systems.

Meskerem Abebaw Mebratie1, Rüdiger Nather2, Guido Falk von Rudorff3

  • 1Universität Kassel, Institut für Mathematik, 34109 Kassel, Germany.

Physical Review. E
|March 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel kernel method for machine learning conservation laws in dynamical systems. This approach reduces computational costs and data requirements compared to neural networks.

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.5K
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

8.4K

Related Experiment Videos

Last Updated: May 21, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.5K
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

8.4K

Area of Science:

  • Dynamical Systems
  • Machine Learning
  • Computational Physics

Background:

  • Conservation laws are fundamental in physics and engineering.
  • Machine learning offers new tools for discovering these laws from data.
  • Existing neural network approaches can be computationally intensive and data-hungry.

Purpose of the Study:

  • To develop an alternative machine learning approach for discovering conservation laws.
  • To reduce the computational cost and data requirements for identifying conservation laws.
  • To explore kernel methods as a viable alternative to neural networks for this task.

Main Methods:

  • Utilizing trajectory data from finite-dimensional dynamical systems.
  • Employing kernel methods, specifically an "indeterminate" form of kernel ridge regression.
  • Minimizing the coefficient vector length to identify conservation laws.

Main Results:

  • Successfully discovered a single conservation law using the proposed method.
  • Demonstrated lower computational costs compared to traditional neural network approaches.
  • Showcased reduced training data requirements for effective learning.

Conclusions:

  • Kernel methods provide an efficient alternative for machine learning conservation laws.
  • The proposed approach offers a computationally cheaper and data-efficient way to discover physical laws.
  • This method has potential applications in various scientific and engineering domains.