Skip to content

Legato Team

Computational Mechanics

Menu

  • Home
  • Mission & Projects
    • Medical Simulation
    • Multi-scale methods for fracture
    • Error estimation
    • Model reduction methods for non-linear problems
    • Alleviate the Mesh Burden in Computational Mechanics
    • Multi-Physics / Multi-Field Problems
    • Meshless Methods
    • Moving Boundary Problems
  • News
  • People
    • Luxembourg team
    • Cardiff team
    • Associated members
    • Visiting professors
    • Collaborators
    • Post-docs
    • PhD students
    • Alumni
  • Publications
  • Resources
    • Software
    • Hardware
  • Media
  • Contacts

FEniCS shells; Bayesian inversion for hyper-elastic solids using dolfin-adjoint

fenics-shells: a UFL-based library for simulating thin structures

http://hdl.handle.net/10993/21554

A Bayesian inversion approach to recovering material parameters in hyperelastic solids using dolfin-adjoint

http://hdl.handle.net/10993/21555

Recent Posts

  • TWINNING workshop in Luxembourg
  • ERCIM “Alain Bensoussan” Fellowship Programme
  • IWR School 2019: “A Crash Course in Machine Learning with Applications in Natural- and Life Sciences (ML4Nature)”
  • Michael Ortiz “Model-Free Data-Driven Computing”
  • Mock ERC panel at UL

inCERT references categories

  • Adaptivity for real-time simulation
  • Augmented reality for computer assisted surgery
  • Bayesian – Markov – surgical simulation
  • Biomechanics advanced constitutive models
  • Boundary elements for non-linear problems
  • Brain
  • Computer-aided diagnosis
  • Contact modelling and simulation
  • Cutting simulation
  • Error estimation for biomechanics
  • FEniCS and dolfin
  • Image registration
  • Implicit methods for real-time simulations
  • inCERT publications
  • Inverse problems in biomechanics
  • Mesh generation
  • Meshless methods
  • Model reduction for deformable models
  • Model reduction for non-linear problems
  • Model reduction of Multi-scale Problems
  • Needle insertion
  • Poro-elasticity
  • Real-time soft tissue deformation simulation
  • Robotic surgery
  • Soft tissue failure simulation
  • Spring-Mass and Mass-Tensor approaches to real-time simulation
  • Surgical training and simulation

Search

Recent posts

  • TWINNING workshop in Luxembourg
  • ERCIM “Alain Bensoussan” Fellowship Programme
  • IWR School 2019: “A Crash Course in Machine Learning with Applications in Natural- and Life Sciences (ML4Nature)”
  • Michael Ortiz “Model-Free Data-Driven Computing”
  • Mock ERC panel at UL

Menu

  • Home
  • Mission & Projects
  • News
  • People
  • Publications
  • Resources
  • inCERT publications
  • Media
  • Contacts
  • Mentions légales

Contact

6 Rue Richard Coudenhove-Kalergi

1359 Luxembourg

Luxembourg

Social

  • Twitter
  • Facebook
  • Linkedin
  • Youtube
Copyright © 2020 Legato Team. Powered by WordPress. Theme: Accelerate by ThemeGrill.
  • Home
  • Mission & Projects
  • News
  • People
  • Publications
  • Resources
  • inCERT publications
  • Media
  • Contacts
  • Mentions légales
This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Cookie settingsACCEPT
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are as essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may have an effect on your browsing experience.
Necessary
Always Enabled

Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.