Modular machine learning-based elastoplasticity: generalization in the context of limited data
From MaRDI portal
Publication:2693407
DOI10.1016/j.cma.2023.115930OpenAlexW4319878959MaRDI QIDQ2693407
Craig M. Hamel, Nikolaos Bouklas, Jan N. Fuhg, Kyle L. Johnson, Reese E. Jones
Publication date: 20 March 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2210.08343
neural networksnon-associative plasticitynonlinear kinematic hardeningsolid mechanicsphysics-informed machine learningdata-driven constitutive models
Related Items (7)
A comparative study on different neural network architectures to model inelasticity ⋮ A neural kernel method for capturing multiscale high-dimensional micromorphic plasticity of materials with internal structures ⋮ Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement ⋮ Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation ⋮ A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content ⋮ Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria ⋮ Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Mechanics of anisotropic materials
- Non-saturating nonlinear kinematic hardening laws
- A review of some plasticity and viscoplasticity constitutive theories
- Time-independent constitutive theories for cyclic plasticity
- A nonlinear kinematic hardening theory for cyclic thermoplasticity and thermoviscoplasticity
- Artificial neural network as an incremental nonlinear constitutive model for a finite element code.
- Two material models for cyclic plasticity: Nonlinear kinematic hardening and generalized plasticity
- Model-free data-driven inelasticity
- Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
- Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks
- Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks
- Learning non-Markovian physics from data
- Learning constitutive relations using symmetric positive definite neural networks
- The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
- On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
- Physics-informed neural networks for inverse problems in supersonic flows
- MAP123-EP: a mechanistic-based data-driven approach for numerical elastoplastic analysis
- A machine learning based plasticity model using proper orthogonal decomposition
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- Model-data-driven constitutive responses: application to a multiscale computational framework
- A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
- Neural networks for topology optimization
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A genetic algorithm for the characterization of hyperelastic materials
- Data-driven computational mechanics
- On nonlinear kinematic hardening
- Machine learning constitutive models of elastomeric foams
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity
- Enhancing phenomenological yield functions with data: challenges and opportunities
- Computational homogenization of nonlinear elastic materials using neural networks
- The theory of plasticity which takes into account residual microstresses
- Mechanics of Solid Materials
- Plastic Internal Variables Formalism of Cyclic Plasticity
- Cyclic Viscoplastic Constitutive Equations, Part I: A Thermodynamically Consistent Formulation
- Numerical implementation of a neural network based material model in finite element analysis
- Historical Development of the Newton–Raphson Method
- Material Modeling via Thermodynamics-Based Artificial Neural Networks
- Matrixungleichungen und die Konvexität der Fließfläche
- A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling
This page was built for publication: Modular machine learning-based elastoplasticity: generalization in the context of limited data
