Characterizing internal structures and defects in materials is a challenging task, often
requiring solutions to inverse problems with unknown topology, geometry, material …

We propose derivative-informed neural operators (DINOs), a general family of neural
networks to approximate operators as infinite-dimensional mappings from input function …

We explore using neural operators, or neural network representations of nonlinear maps
between function spaces, to accelerate infinite-dimensional Bayesian inverse problems …

We reconstruct the in vivo spatial distribution of linear and nonlinear elastic parameters in
ten patients with benign (five) and malignant (five) tumors. The mechanical behavior of …

The inverse identification of heterogeneous composite properties from measured
displacement/strain fields is pivotal in engineering. Traditional methodologies and emerging …

The ability to make decisions based on quantities of interest that depend on variables
inferred from measurement finds application in different fields of mechanics and physics …

We report a summary of recent developments and current status of our team's efforts to
image and quantify in vivo nonlinear strain and tissue mechanical properties. Our work is …

This paper presents a method to derive the virtual fields for identifying constitutive model
parameters using the Virtual Fields Method (VFM). The VFM is an approach to identify …

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

We present a novel approach to solve the inverse problem in finite elasticity for the non-
homogeneous shear modulus distribution solely from known surface deformation fields. The …