3 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
Hetero-EUCLID: Interpretable model discovery for heterogeneous hyperelastic materials using stress-unsupervised learning
Kanhaiya Lal Chaurasiya, Saurav Dutta, Siddhant Kumar +1
We propose a computational framework, Hetero-EUCLID, for segmentation and parameter identification to characterize the full hyperelastic behavior of all constituents of a heterogen…