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 Experiment Videos

A computationally efficient optimization kernel for material parameter estimation procedures.

H Schmid1, M P Nash, A A Young

  • 1Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1001 New Zealand. h.schmid@auckland.ac.nz

Journal of Biomechanical Engineering
|April 6, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same author

Probing the Shape of the Weyl Fermi Surface of NbP Using Transverse Electron Focusing.

Physical review letters·2024
Same author

Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure.

Medical image analysis·2023
Same author

The Physician and His Microscope.

Buffalo medical journal·2023
Same author

Herpes Facialis.

Buffalo medical journal·2023
Same author

Erysipelas.

Buffalo medical journal·2023
Same journal

Computational Determination of Effective Working Length in Experimental Torsion Testing of Long Bones.

Journal of biomechanical engineering·2026
Same journal

Hierarchical Experimental Characterization of the Human Rib Cage for Nonlethal Projectile Impact Applications.

Journal of biomechanical engineering·2026
Same journal

An in vitro Experimental Model for Investigating Aortic Pressure Dynamics Under Blunt Thoracic Impacts.

Journal of biomechanical engineering·2026
Same journal

Editorial.

Journal of biomechanical engineering·2026
Same journal

Student Paper Competition of the 2025 ASME SB3C Summer Bioengineering Conference.

Journal of biomechanical engineering·2026
Same journal

Biomechanical Principles of Temporal Muscle Activation in Functional Movements: Implications for Stability and Movement Coordination.

Journal of biomechanical engineering·2026
See all related articles

Estimating soft tissue material parameters is crucial but time-consuming. A new weighted least-squares method using Gaussian quadrature significantly reduces computation time by 100-fold.

Area of Science:

  • Biomechanics
  • Computational mechanics
  • Biomaterials science

Background:

  • Accurate estimation of soft tissue material parameters is essential for understanding tissue mechanics.
  • Traditional methods for parameter estimation are computationally intensive, often requiring days of processing time.

Purpose of the Study:

  • To develop a computationally efficient method for estimating soft tissue material parameters.
  • To reduce the computational burden associated with material parameter estimation in soft tissue mechanics.

Main Methods:

  • The study applies a modified least-squares approach, specifically a weighted least-squares objective function.
  • The objective function, an L(2)-norm type integral, is approximated using Gaussian quadrature.
  • The method is exemplified using homogeneous simple shear experiments to determine orthotropic constitutive parameters of myocardium.

Related Experiment Videos

Main Results:

  • The proposed weighted least-squares method with Gaussian quadrature approximation significantly reduces computational time.
  • Computational time for material parameter estimation is decreased by two orders of magnitude (100-fold).

Conclusions:

  • The novel approach drastically improves the efficiency of soft tissue material parameter estimation.
  • This method offers a practical solution for complex biomechanical modeling, enabling faster analysis of soft tissue properties.