Sparse regression and feature extraction are the cornerstones of knowledge discovery from
massive data. Their goal is to discover interpretable and predictive models that provide …

The two fundamental concepts of materials theory, pseudo potentials and the assumption of
a multiplicative decomposition, allow a general description of inelastic material behavior …

In contemporary elasticity theory, the strain–energy function predominantly relies on the first
invariant I 1 of the deformation tensor; a practice that has been influenced by models derived …

Data-driven models based on deep learning algorithms intend to overcome the limitations of
traditional constitutive modelling by directly learning from data. However, the need for …

For more than half a century, scientists have developed mathematical models to understand
the behavior of the human heart. Today, we have dozens of heart tissue models to choose …

To comprehensively consider the impacts of stratification, residual pore water pressure, soil
nonlinearity, and boundary permeability on consolidation settlement of soft soil foundations …

The ability to automatically discover interpretable mathematical models from data could
forever change how we model soft matter systems. For convex discovery problems with a …

Elastomers are made of chain-like molecules to form networks that can sustain large
deformation. Rubbers are thermosetting elastomers that are obtained from irreversible …

Hyperelastic structures can exhibit peculiar nonlinear vibration and acoustic behavior due to
the existence of geometric and material nonlinearities. In order to comprehend the nonlinear …

Personalized computational simulations have emerged as a vital tool to understand the
biomechanical factors of a disease, predict disease progression, and design personalized …