38 citations · 41 across the 6 of their papers we have counts for
6 papers
Flow-based conditional cardiac anatomy generation for virtual cohorts
Konstantinos Kevopoulos, Beatrice Moscoloni, Benjamin Alheit +4
Cardiac digital twin research is moving from subject-specific anatomical replicas toward virtual cohorts that represent clinically relevant population subgroups. Yet access to repr…
CANN-EUCLID: unsupervised constitutive artificial neural network model discovery from full-field data
Benjamin Alheit, Siddhant Kumar, Mathias Peirlinck
Constitutive artificial neural networks (CANNs) provide interpretable material model discovery, but have so far been used in stress-supervised settings based on apparent stress-str…
Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study
Rogier P. Krijnen, Akshay Joshi, Siddhant Kumar +1
Cardiac muscle tissue exhibits highly non-linear hyperelastic and orthotropic material behavior during passive deformation. Traditional constitutive identification protocols theref…
COMMET: orders-of-magnitude speed-up in finite element method via batch-vectorized neural constitutive updates
Benjamin Alheit, Mathias Peirlinck, Siddhant Kumar
Constitutive evaluations often dominate the computational cost of finite element (FE) simulations whenever material models are complex. Neural constitutive models (NCMs) offer a hi…
Full-field surrogate modeling of cardiac function encoding geometric variability
Elena Martinez, Beatrice Moscoloni, Matteo Salvador +3
Combining physics-based modeling with data-driven methods is critical to enabling the translation of computational methods to clinical use in cardiology. The use of rigorous differ…
Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)
Prakash Thakolkaran, Yaqi Guo, Shivam Saini +3
Traditional constitutive models rely on hand-crafted parametric forms with limited expressivity and generalizability, while neural network-based models can capture complex material…