Jove
Visualize
Contact Us

Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

354
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
354
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

183
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
183
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
48
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
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,...
81
Modeling and Similitude01:12

Modeling and Similitude

261
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
261
Mechanical Systems01:22

Mechanical Systems

190
Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
190

You might also read

Related Articles

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

Sort by
Same author

Real-time CBCT reconstructions using Krylov solvers in repeated scanning procedures.

Physics in medicine and biology·2026
Same author

Artificial Intelligence-Led Whole Coronary Artery OCT Analysis; Validation and Identification of Drug Efficacy and Higher-Risk Plaques.

Circulation. Cardiovascular imaging·2025
Same author

Partial differential equations in data science.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2025
Same author

Enhancing Brain Age Prediction and Neurodegeneration Detection with Contrastive Learning on Regional Biomechanical Properties.

bioRxiv : the preprint server for biology·2025
Same author

Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition.

Heritage science·2023
Same author

On Krylov methods for large-scale CBCT reconstruction.

Physics in medicine and biology·2023
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 Experiment Video

Updated: Jun 22, 2025

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

3.4K

Can physics-informed neural networks beat the finite element method?

Tamara G Grossmann1, Urszula Julia Komorowska2, Jonas Latz3

  • 1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK.

IMA Journal of Applied Mathematics
|June 27, 2024
PubMed
Summary

This study compares numerical methods for solving partial differential equations (PDEs). Physics-informed neural networks (PINNs) were not found to outperform the established finite element method (FEM) in terms of accuracy and speed.

Keywords:
deep learningfinite element methodpartial differential equationsphysics-informed neural networks

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.7K
Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
06:34

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes

Published on: January 6, 2023

1.6K

Related Experiment Videos

Last Updated: Jun 22, 2025

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

3.4K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.7K
Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
06:34

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes

Published on: January 6, 2023

1.6K

Area of Science:

  • Computational Mathematics
  • Numerical Analysis
  • Scientific Computing

Background:

  • Partial differential equations (PDEs) are essential for modeling scientific phenomena.
  • Numerical methods approximate PDE solutions for simulations.
  • Physics-informed neural networks (PINNs) are a recent deep learning approach for PDEs.

Purpose of the Study:

  • To systematically compare the performance of PINNs and the finite element method (FEM).
  • To evaluate computational costs and approximation accuracies of both methods on various PDEs.

Main Methods:

  • Solving 1D, 2D, and 3D Poisson equations.
  • Solving 1D Allen-Cahn and 1D/2D semilinear Schrödinger equations.
  • Utilizing both PINNs and the FEM for numerical approximation.

Main Results:

  • The FEM generally achieved superior accuracy and solution times compared to PINNs.
  • PINNs demonstrated faster evaluation of the solved PDE in specific experimental setups.
  • No significant advantage of PINNs over FEM was observed in this comparative study.

Conclusions:

  • The finite element method remains a robust and efficient choice for solving a wide range of PDEs.
  • While promising, physics-informed neural networks require further development to consistently match or exceed FEM performance.
  • This study highlights the need for continued research in both numerical methods and deep learning for scientific computing.