An Eulerian constitutive model for rate-dependent inelasticity enhanced by neural networks
From MaRDI portal
Publication:6595904
DOI10.1016/j.cma.2024.117241MaRDI QIDQ6595904
Publication date: 30 August 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Optimization by Simulated Annealing
- Modeling a smooth elastic-inelastic transition with a strongly objective numerical integrator needing no iteration
- On the kinematics of finite strain plasticity
- Allgemeine Kontinuumstheorie der Versetzungen und Eigenspannungen
- A framework for finite strain elastoplasticity based on maximum plastic dissipation and the multiplicative decomposition. II: Computational aspects
- Truncated-Newton training algorithm for neurocomputational viscoplastic model.
- Artificial neural network as an incremental nonlinear constitutive model for a finite element code.
- Model-free data-driven inelasticity
- Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning
- On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
- A machine learning based plasticity model using proper orthogonal decomposition
- Unsupervised discovery of interpretable hyperelastic constitutive laws
- Data driven computing with noisy material data sets
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Data-driven computational mechanics
- Numerical implementation of a neural network based material model in finite element analysis
- CALCULATION OF HYPERELASTIC RESPONSE OF FINITELY DEFORMED ELASTIC-VISCOPLASTIC MATERIALS
- Deep Learning in Computational Mechanics
- Equation of State Calculations by Fast Computing Machines
- Elastic-Plastic Deformation at Finite Strains
- Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
- An Eulerian constitutive model for the inelastic finite strain behaviour of isotropic semi-crystalline polymers
- A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity
- Viscoelastic constitutive artificial neural networks (vCANNs) -- a framework for data-driven anisotropic nonlinear finite viscoelasticity
- Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified constitutive relation error framework
- Data-driven computing in dynamics
This page was built for publication: An Eulerian constitutive model for rate-dependent inelasticity enhanced by neural networks