Background and objectives Finite-element analysis (FEA) is widely used as a standard tool
for stress and deformation analysis of solid structures, including human tissues and organs …

Constitutive material modeling is an important basis for mechanical analysis. Machine
learning methods based on neural networks have been extensively used for the discovery of …

Identifying unknown Young's moduli and boundary conditions of the heterogeneous material
using locally observed boundary data is the inverse problem which is generally solved by …

Although there have been many studies on the identification of elastic constants and
boundary conditions, it is still challenging to simultaneously estimate the unknown elastic …

Quasi-static elasticity imaging techniques rely on model-based mathematical inverse
methods to estimate mechanical parameters from force-displacement measurements. These …

Simultaneously identifying unknown Young's modulus and boundary conditions of a linear
elastic material with the measurements only on a part of its boundary is an inverse problem …

The identification accuracy of material model parameters is essential for accurately
predicting the deformation behavior of metallic materials (eg, metal forming) using a finite …

In this work, we present a novel inverse approach to characterize the nonhomogeneous
mechanical behavior of linear elastic solids. In this approach, we optimize the geometric …

Estimating the unknown Young's modulus and boundary conditions of an elastic object from
locally observed boundary data is an inverse problem which is usually solved by iterative …

This paper presents a comparative study of two typical inverse algorithms, ie, direct and
iterative inversion methods, to reconstruct the shear modulus distribution of linearly elastic …