113 citations · 243 across the 18 of their papers we have counts for
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Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
Vahidullah Tac, Manuel K Rausch, Ilias Bilionis +2
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy fu…
Benchmarks for physics-informed data-driven hyperelasticity
Vahidullah Tac, Kevin Linka, Francisco Sahli-Costabal +2
Data-driven methods have changed the way we understand and model materials. However, while providing unmatched flexibility, these methods have limitations such as reduced capacity…
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
Vahidullah Tac, Manuel K. Rausch, Francisco Sahli-Costabal +1
We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy fun…