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Found 5 papers, 3 papers with code
Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries.
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks.
Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations
In this study, we use a novel class of neural networks, known as neural ordinary differential equations (N-ODEs), to develop data-driven material models that automatically satisfy polyconvexity of the strain energy function with respect to the deformation gradient, a condition needed for the existence of minimizers for boundary value problems in elasticity.
Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue
The neural network material model can then be interpreted as the best extension of an expert model: it learns the features that an expert has encoded in the analytical model while fitting the experimental data better.
Predicting the Mechanical Properties of Biopolymer Gels Using Neural Networks Trained on Discrete Fiber Network Data
In this work, the FCNN trained on the discrete fiber network data was used in finite element simulations of fibrin gels using our UMAT.
