A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials
DOI10.1016/J.CMA.2024.117246zbMATH Open1544.74003MaRDI QIDQ6595912
Zhong-Xuan Yang, Author name not available (Why is that?), T. H. Chen, Y. Yu, M. M. Su
Publication date: 30 August 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
elastoplasticitydata-driven constitutive modelingstored plastic workstress probing methodthermodynamics-informed neural network
Thermal effects in solid mechanics (74F05) Granularity (74E20) Mathematical modeling or simulation for problems pertaining to mechanics of deformable solids (74-10)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Dissipation consistent fabric tensor definition from DEM to continuum for granular media
- Computational inelasticity
- A thermomechanical framework for constitutive models for rate-independent dissipative materials
- 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
- Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks
- Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN)
- Unsupervised discovery of interpretable hyperelastic constitutive laws
- A physics-constrained data-driven approach based on locally convex reconstruction for noisy database
- Energy dissipation analysis of elastic-plastic materials
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An assessment of plasticity theories for modeling the incrementally nonlinear behavior of granular soils
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials
- A new family of constitutive artificial neural networks towards automated model discovery
- Automated discovery of generalized standard material models with EUCLID
- Enhancing phenomenological yield functions with data: challenges and opportunities
- Physical reasons for abandoning plastic deformation measures in plasticity and viscoplasticity theory
- A coupled FEM/DEM approach for hierarchical multiscale modelling of granular media
- A Micromechanical Description of Granular Material Behavior
- Application of thermomechanical principles to the modelling of geotechnical materials
- A theoretical framework for constructing elastic/plastic constitutive models of triaxial tests
- Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
- The Derivation of Constitutive Relations from the Free Energy and the Dissipation Function
- A thermomechanical framework for rate-independent dissipative materials with internal functions
- A comparative study on different neural network architectures to model inelasticity
- Incremental neural controlled differential equations for modeling of path-dependent material behavior
- Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
- Neural integration for constitutive equations using small data
- An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method
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