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Found 4 papers, 1 papers with code
Inverse Design of Vitrimeric Polymers by Molecular Dynamics and Generative Modeling
Vitrimer is a new class of sustainable polymers with the ability of self-healing through rearrangement of dynamic covalent adaptive networks.
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.
Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy
We present a two-scale topology optimization framework for the design of macroscopic bodies with an optimized elastic response, which is achieved by means of a spatially-variant cellular architecture on the microscale.
Computational Engineering, Finance, and Science
