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

Postsynaptic Potential (PSP)

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...

Neuroplasticity

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

You might also read

Related Articles

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

Sort by

Same author

ForestSAR-YOLO: real-time detection of small human targets in UAV imagery.

Scientific reports·2026

Same author

Assessing the onset of spring water-level rise in snowmelt-dominated rivers of northeastern Russia using machine learning.

Scientific reports·2026

Same author

Multi-objective optimization of a regional biogas supply chain using organic waste.

Scientific reports·2026

Same author

A review of applications of machine learning in quantum dots research.

Discover nano·2026

Same author

Dual-loop proportional control for high-precision induction brazing of thin-walled aluminum waveguides.

Scientific reports·2026

Same author

Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review.

Polymers·2025

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

Published on: October 25, 2017

Physics-Informed Neural Networks in Polymers: A Review.

Ivan Malashin¹, Vadim Tynchenko¹, Andrei Gantimurov¹

¹Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.

|April 26, 2025

Summary

This summary is machine-generated.

Physics-informed neural networks (PINNs) offer a novel approach to polymer modeling, integrating data with physical laws. This review explores PINNs

Keywords:

ML in materials science multi-scale simulation physics-informed neural networks (PINNs)polymer modeling structure–property relationships

More Related Videos

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

Published on: December 8, 2017

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Related Experiment Videos

Last Updated: Jul 7, 2026

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

Published on: October 25, 2017

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

Published on: December 8, 2017

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Two-Photon Polymerization 3D-Printing of Micro-scale Neuronal Cell Culture Devices

Published on: June 7, 2024

Area of Science:

Polymer Science and Engineering
Computational Materials Science
Artificial Intelligence in Chemistry

Background:

Polymer systems exhibit complex multi-scale behavior, posing significant challenges for traditional modeling and simulation techniques.
Balancing accuracy and computational efficiency remains a key hurdle in bridging atomistic and macroscopic scales for polymer systems.

Purpose of the Study:

To review the development and application of physics-informed neural networks (PINNs) in polymer science.
To summarize recent advances, methodologies, benefits, and limitations of PINNs for polymer modeling.
To identify future research directions for advanced polymer simulations using PINNs.

Main Methods:

Review of existing literature on PINNs applied to polymer systems.
Analysis of methodologies integrating data-driven learning with physical laws.
Evaluation of PINNs for property prediction, structural design, and process optimization.

Main Results:

PINNs show promise in overcoming limitations of traditional methods by combining data-driven insights with physical principles.
Applications span polymer property prediction, structural design, and process optimization.
Key methodologies and benefits are identified, alongside current limitations.

Conclusions:

PINNs represent a powerful emerging tool for advanced polymer modeling and simulation.
Further research is needed to address current challenges and fully exploit PINNs' potential in polymer science.
PINNs offer a pathway to more accurate and efficient simulations across multiple scales.