60 citations · 60 across the 2 of their papers we have counts for
4 papers
On Parameter Identification in Three-Dimensional Elasticity and Discretisation with Physics-Informed Neural Networks
Federica Caforio, Martin Holler, Matthias Höfler
Physics-informed neural networks have emerged as a powerful tool in the scientific machine learning community, with applications to both forward and inverse problems. While they ha…
Non-Intrusive Parametrized-Background Data-Weak Reconstruction of Cardiac Displacement Fields from Sparse MRI-like Observations
Francesco C. Mantegazza, Federica Caforio, Christoph Augustin +3
Personalized cardiac diagnostics require accurate reconstruction of myocardial displacement fields from sparse clinical imaging data, yet current methods often demand intrusive acc…
Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models
Matthias Höfler, Francesco Regazzoni, Stefano Pagani +5
Active stress models in cardiac biomechanics account for the mechanical deformation caused by muscle activity, thus providing a link between the electrophysiological and mechanical…
Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models
Federica Caforio, Francesco Regazzoni, Stefano Pagani +5
The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the in…