MS- 4-3

Mini-symposium title 

4-3 – Non-invasive and Inverse Methods for Constitutive Parameter Identification       

Organisers 

Sam Evans (Cardiff University, UK), Stéphane Avril (MINES Saint-Étienne), Kevin Moerman (NUI Galway), Dana Solav (Technion – Israel Institute of Technology)   

Mini-symposium description 

Inverse problems arising in hybrid experimental - computational methods become more and more popular in many fields of mechanics, including mechanobiology. 

 

It has become a common practice to combine image based full-field displacement measurements custom inverse methods to infer (using nonlinear regression) the best-fit material parameters and the rupture stresses and strains. Such approaches also exist for characterizing the material parameters of soft biological tissues in vivo, where advanced medical imaging can provide precise measurements of tissue deformation under different modes of action, and inverse methodologies are used to derive material properties from those data.  

 

Nowadays, these approaches offer important possibilities which aims at gaining better insight in the regionally varying stiffness, strength and time-dependent properties of many materials. Important challenges in experimental mechanics are now to develop and implement hybrid experimental - computational method to quantify regional variations in properties in situ. The main motivation of the symposium is to review the latest progress and permit scientific discussions on these methods by bringing together researchers interested by characterizing material properties of soft tissues.  

 

Topics to be considered are related to the different challenges posed by inverse problems in soft tissue biomechanics and mechanobiology, such as: 

 

  • optimization approaches and model order reduction 

  • model fitting against uncertain experimental results  

  • uniqueness of identified parameters  

  • reliability of computational models for biological tissues.  

  • uncertainty assessment in inverse problems  

  • optical full-field strain measurements  

  • digital image/volume correlation 

  • in vivo identification using medical imaging 

  • virtual fields method 

  • regularization approaches 

  • inverse stress analysis, inverse deformation problem 

  • machine-learning and data-driven discovery in material characterization   

  • hyperelastic image registration.

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