Data-driven tissue mechanics with polyconvex neural ordinary differential equations
From MaRDI portal
Publication:2160446
DOI10.1016/j.cma.2022.115248OpenAlexW4285384816WikidataQ115358842 ScholiaQ115358842MaRDI QIDQ2160446
Vahidullah Tac, Adrian B. Tepole, Francisco Sahli Costabal
Publication date: 3 August 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2110.03774
Related Items
Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data ⋮ Advanced discretization techniques for hyperelastic physics-augmented neural networks ⋮ A new family of constitutive artificial neural networks towards automated model discovery ⋮ A comparative study on different neural network architectures to model inelasticity ⋮ Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics ⋮ Automated model discovery for skin: discovering the best model, data, and experiment ⋮ Discovering the mechanics of artificial and real meat ⋮ Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations ⋮ \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining ⋮ On automated model discovery and a universal material subroutine for hyperelastic materials ⋮ Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria ⋮ Automated discovery of generalized standard material models with EUCLID ⋮ Incremental neural controlled differential equations for modeling of path-dependent material behavior ⋮ Neural integration for constitutive equations using small data
Uses Software
Cites Work
- Convexity conditions and existence theorems in nonlinear elasticity
- A computational framework for polyconvex large strain elasticity
- Invariant formulation of hyperelastic transverse isotropy based on polyconvex free energy functions.
- Machine learning materials physics: integrable deep neural networks enable scale bridging by learning free energy functions
- On the convexity of nonlinear elastic energies in the right Cauchy-Green tensor
- Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures
- A generic physics-informed neural network-based constitutive model for soft biological tissues
- Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity
- Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
- Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data
- Hyperelastic energy densities for soft biological tissues: a review
- An illustration of the equivalence of the loss of ellipticity conditions in spatial and material settings of hyperelasticity
- On the uniqueness of energy minimizers in finite elasticity
- A polyconvex framework for soft biological tissues. Adjustment to experimental data
- Quasi-convexity and the lower semicontinuity of multiple integrals
- A polyconvex formulation of isotropic elastoplasticity theory
- Computational homogenization of nonlinear elastic materials using neural networks
- Constitutive modelling of passive myocardium: a structurally based framework for material characterization
- Frame-Invariant Polyconvex Strain-Energy Functions for Some Anisotropic Solids
- On Isotropic, Frame-Invariant, Polyconvex Strain-Energy Functions
- The Convexity Properties of a Class of Constitutive Models for Biological Soft Issues
- Turbulence Modeling in the Age of Data
- Large elastic deformations of isotropic materials IV. further developments of the general theory
- Calculus of variations
- A new constitutive framework for arterial wall mechanics and a comparative study of material models
- Biaxial mechanical evaluation of planar biological materials
