Automated discovery of generalized standard material models with EUCLID
From MaRDI portal
Publication:2683452
DOI10.1016/j.cma.2022.115867OpenAlexW4313681194MaRDI QIDQ2683452
Moritz Flaschel, Siddhant Kumar, Laura De Lorenzis
Publication date: 10 February 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2211.04453
inverse problemsunsupervised learningsparse regressiongeneralized standard materialsconstitutive modelsinterpretable models
Related Items (9)
Advanced discretization techniques for hyperelastic physics-augmented neural networks ⋮ Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement ⋮ Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics ⋮ Discovering stochastic partial differential equations from limited data using variational Bayes inference ⋮ 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 ⋮ Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response ⋮ Neural integration for constitutive equations using small data ⋮ Viscoelastic constitutive artificial neural networks (vCANNs) -- a framework for data-driven anisotropic nonlinear finite viscoelasticity
Uses Software
Cites Work
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Formulation of thermoelastic dissipative material behavior using GENERIC
- Extension of the virtual fields method to elasto-plastic material identification with cyclic loads and kinematic hardening
- Coupled viscoelastic-viscoplastic modeling of homogeneous and isotropic polymers: numerical algorithm and analytical solutions
- An attempt to generalize Onsager's principle, and its significance for rheological problems
- Learning constitutive relations from indirect observations using deep neural networks
- Computational inelasticity
- A manifold learning approach to data-driven computational elasticity and inelasticity
- Big data in experimental mechanics and model order reduction: today's challenges and tomorrow's opportunities
- Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
- Finite electro-elasticity with physics-augmented neural networks
- Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks
- AutoMat: automatic differentiation for generalized standard materials on GPUs
- Structure-preserving neural networks
- Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties
- Data-driven tissue mechanics with polyconvex neural ordinary differential equations
- Model-data-driven constitutive responses: application to a multiscale computational framework
- Unsupervised discovery of interpretable hyperelastic constitutive laws
- Systems with internal parameters obeying the orthogonality condition
- Data-based derivation of material response
- Model-free data-driven methods in mechanics: material data identification and solvers
- Data-driven computational mechanics
- A model-reduction approach in micromechanics of materials preserving the variational structure of constitutive relations
- Neural network constitutive modelling for non-linear characterization of anisotropic materials
- Thermodynamik und rheologische Probleme
- A model of incompressible isotropic hyperelastic material behavior using spline interpolations of tension-compression test data
- Reciprocal Relations in Irreversible Processes. II.
- A Statistical View of Some Chemometrics Regression Tools
- What Machine Learning Can Do for Computational Solid Mechanics
- Numerical studies on the identification of the material parameters of Rivlin's hyperelasticity using tension-torsion tests
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Automated discovery of generalized standard material models with EUCLID