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

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

774
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
774
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

103
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...
103
Solving Problems in Physics02:32

Solving Problems in Physics

7.1K
Problem-solving is the ability to apply general physical principles to specific situations, usually expressed by equations. It is an essential skill in physics, and can also be useful for applying physics in everyday life as well. Analytical skills and problem-solving abilities can be applied to new situations, compared to a list of facts, which can never be extensive enough to include every possible circumstance. To solve physics problems, a certain amount of creativity and insight is...
7.1K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

879
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
879
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

132
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
132
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.8K

You might also read

Related Articles

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

Sort by
Same author

[Treatable traits associated with acute exacerbations of bronchiectasis].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases·2025
Same author

[Effect of recombinant human thrombin for hemostasis in liver resection: a randomized controlled phase â…¢ clinical trial].

Zhonghua yi xue za zhi·2023
Same author

[Strategy of schistosomiasis elimination and its effects in Jinhu County, Jiangsu Province].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2019
Same author

[A clinical analysis of hepatic veno-occlusive disease after hematopoietic stem cell transplantation].

Zhonghua nei ke za zhi·2018
Same author

Serum Dickkopf-1 levels as a clinical and prognostic factor in patients with bladder cancer.

Genetics and molecular research : GMR·2016
Same author

Characterization of agronomic and quality traits and HSW-G5 compositions from the progenies of common wheat (Triticum aestivum L.) with different protein content.

Genetics and molecular research : GMR·2015

Related Experiment Video

Updated: Sep 18, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.1K

Physics-informed neural networks for solving inverse problems in phase field models.

B R Zhao1, D K Sun1, H Wu1

  • 1Key Laboratory of Structure and Thermal Protection of High Speed Aircraft, Ministry of Education, School of Mechanical Engineering, Southeast University, Nanjing, 211189, Jiangsu, China; Jiangsu Key Laboratory for Biomaterials and Devices, Southeast University, Dingjiaqiao 87, Nanjing, 210009, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 24, 2025
PubMed
Summary

This study uses Physics-Informed Neural Networks (PINNs) to solve inverse problems in materials science, uncovering physical laws and accurately inverting material parameters. The approach is validated for anisotropic and multi-physics systems.

Keywords:
Deep learningLattice BoltzmannPINNsPhase field

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

1.8K
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.0K

Related Experiment Videos

Last Updated: Sep 18, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.1K
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

1.8K
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.0K

Area of Science:

  • Materials Science
  • Computational Physics
  • Machine Learning

Background:

  • Physics-Informed Neural Networks (PINNs) are increasingly used in materials science.
  • Current research often focuses on forward problems or prediction accuracy.
  • There's a need to apply PINNs to inverse problems for deeper material understanding.

Purpose of the Study:

  • To apply PINNs to inverse problems in numerical simulation modeling.
  • To uncover underlying physical laws from data using integrated neural networks.
  • To validate PINNs for inverting anisotropic material parameters and in multi-physics systems.

Main Methods:

  • Developed a neural network integrating data-driven and physics-driven modules.
  • Applied PINNs to inverse problems in diffusion, flow, and phase transition simulations.
  • Validated the method on benchmark anisotropic function inversion and multi-physics coupled systems.

Main Results:

  • Successfully uncovered embedded physical laws within simulation data.
  • Achieved high consistency between predicted and theoretical values for anisotropic parameter inversion.
  • Demonstrated PINNs' applicability to inverse problems in multi-physics coupled systems (phase field, temperature, flow).

Conclusions:

  • PINNs are effective for solving inverse problems in materials science simulations.
  • The integrated approach enables the inversion of critical anisotropic and multi-physics material parameters.
  • This work highlights the potential of combining numerical simulation data with deep learning for advancing materials science research.