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Found 3 papers, 1 papers with code
A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids.
Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID
Following the motive of the recently proposed computational framework EUCLID (Efficient Unsupervised Constitutive Law Identitication and Discovery) and in contrast to conventional parameter calibration methods, we construct an extensive set of candidate hyperelastic models, i. e., a model library including popular models known from the literature, and develop a computational strategy for automatically selecting a model from the library that conforms to the available experimental data while being represented as an interpretable symbolic mathematical expression.
NN-EUCLID: deep-learning hyperelasticity without stress data
We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks.
