Skip to main content

Advanced Search

Projected result for your current search: 0

Search Operators:

You can use operators in your search query to narrow down your results even more. Click on the search operator to see an explanation of how it works.
Finds documents with exactly this word group, in exactly this word order and spelling (e.g. "employer branding").
Finds documents with both search terms (e.g. sales AND bonus).
Finds documents with one or the other search term (e.g. porsche OR volkswagen).
Finds documents with all search terms. Blank space is understood as AND (e.g. man robot production).
Finds documents with no appearance of the word behind NOT (e.g. ford NOT "harrison ford").
Finds documents where the search term is mentioned at least "n" times."n" may be any number (e.g. COUNT(gear)>8).
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
Finds documents with the search term in different spellings. "?" allows only one character (e.g. organi?ation).
Special characters are understood as AND (e.g. Miller Bros. & Sons).

Semantic Search

powered by (Link opens in a new window)
loading …
Log in
Top
Experimental Mechanics

Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural Network

  • 09-09-2024
  • Research paper
Published in:
Authors
C. Jailin
A. Benady
R. Legroux
E. Baranger
Published in
Experimental Mechanics | Issue 9/2024

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The article introduces a method for modeling hyperelastic behavior using Physics-Augmented Neural Networks (PANNs) within the EUCLID framework. It details the experimental setup, including uni-axial tensile tests on neoprene rubber material, and the use of Digital Image Correlation (DIC) for measuring displacement fields. The methodology involves training various PANN architectures and comparing their performance with a traditional Neo-Hookean model. The results show that PANN models can effectively learn and generalize complex material behaviors, outperforming traditional models in certain aspects. The article also discusses the challenges and limitations of the current approach, suggesting future research directions to improve the robustness and applicability of PANN models in materials science.
AI Generated

This summary of the content was generated with the help of AI.

Already a customer? Then log in here to gain access.

Customer login

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Business + Economics & Engineering + Technology"

Online-Abonnement

Springer Professional "Business + Economics & Engineering + Technology" gives you access to:

  • more than 130.000 books
  • more than 540 journals

from the following subject areas:

  • Automotive
  • Construction + Real Estate
  • Business IT + Informatics
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Mechanical Engineering + Materials
  • Surfaces + Materials Technology
  • Insurance + Risk


Secure your knowledge advantage now!

inform now

Springer Professional "Engineering + Technology"

Online-Abonnement

Springer Professional "Engineering + Technology" gives you access to:

  • more than 75.000 books
  • more than 390 journals

from the following specialised fileds:

  • Automotive
  • Business IT + Informatics
  • Construction + Real Estate
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Mechanical Engineering + Materials
  • Surfaces + Materials Technology





 

Secure your knowledge advantage now!

inform now

For explorers: Get to know our platforms better with a free registration.

Register for free
previous article Preliminary Insight Into Torsion of Additively-Manufactured Polylactic Acid (PLA)-Based Polymers
next article Size Effect on the Ductile Fracture of the Aluminium Alloy 2024-T351
Title
Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural Network
Authors
C. Jailin
A. Benady
R. Legroux
E. Baranger
Publication date
09-09-2024
Publisher
Springer US
Published in
Experimental Mechanics / Issue 9/2024
Print ISSN: 0014-4851
Electronic ISSN: 1741-2765
DOI
https://doi.org/10.1007/s11340-024-01106-5
This content is only visible if you are logged in and have the appropriate permissions.

Other articles of this Issue 9/2024

A Note of Gratitude from the Editor-in-Chief

  • Editorial

Validation of Hole-Drilling Residual Stress Measurements in Workpieces of Various Thickness

  • Open Access
  • Research paper

Size Effects in Strength and Strain Hardening Behavior of Single-Crystal 7075 Aluminum Alloy Micropillars

  • Research paper

Simultaneous Measurement of Fiber-Matrix Interface Debonding and Tunneling Using a Dual-Vision Experimental Setup

  • Open Access
  • Research paper

Biomechanical Hand Model: Modeling and Simulating the Lateral Pinch Movement

  • Open Access
  • Research paper

Measurement of the Tension Loss in a Cable Traveling Over a Pulley, for Low-Speed Applications

  • Open Access
  • Research paper

Premium Partners

    Image Credits
    in-adhesives, MKVS, Ecoclean/© Ecoclean, Hellmich GmbH/© Hellmich GmbH, Krahn Ceramics/© Krahn Ceramics, Kisling AG/© Kisling AG, ECHTERHAGE HOLDING GMBH&CO.KG - VSE, Schenker Hydraulik AG/© Schenker Hydraulik AG