53 citations · 66 across the 7 of their papers we have counts for
7 papers
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter +8
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application o…
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…
Physics-informed neural networks for blood flow inverse problems
Jeremias Garay, Jocelyn Dunstan, Sergio Uribe +1
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known a…
Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields
Tabita Catalán, Matías Courdurier, Axel Osses +3
Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelera…
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…