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Found 4 papers, 1 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 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.
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.
Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty
The optimization task relies on the efficiency of the GP surrogates to calculate the expected cost of different strategies when the uncertainty of other material parameters is included.
