Material Modeling via Thermodynamics-Based Artificial Neural Networks

DOI10.1007/978-3-030-77957-3_16OpenAlexW3176537742MaRDI QIDQ5021908

Victor Maffi-Berthier, Filippo Masi, Paolo Vannucci, Ioannis Stefanou

Publication date: 14 January 2022

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-77957-3_16

Mathematics Subject Classification ID

Computer science (68-XX) Statistical mechanics, structure of matter (82-XX) Differential geometry (53-XX)

Related Items (5)

An FE-DMN method for the multiscale analysis of thermomechanical composites ⋮ Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) ⋮ Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks ⋮ Modular machine learning-based elastoplasticity: generalization in the context of limited data ⋮ Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate

Cites Work

• Unnamed Item
• Unnamed Item
• Artificial neural networks in numerical modelling of composites
• Data-driven non-linear elasticity: constitutive manifold construction and problem discretization
• Learning constitutive relations from indirect observations using deep neural networks
• Thermodynamics with internal variables. I: General concepts. II: Applications
• A multilevel finite element method (FE\(^{2}\)) to describe the response of highly nonlinear structures using generalized continua.
• Artificial neural network as an incremental nonlinear constitutive model for a finite element code.
• A thermomechanical framework for constitutive models for rate-independent dissipative materials
• A manifold learning approach to data-driven computational elasticity and inelasticity
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
• Model-free data-driven inelasticity
• Model-free data-driven computational mechanics enhanced by tensor voting
• Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
• Structure-preserving neural networks
• Learning non-Markovian physics from data
• Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
• \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
• Data-driven computational mechanics
• Numerical implementation of a neural network based material model in finite element analysis
• Approximation by superpositions of a sigmoidal function
• On the micromechanics of deep material networks

This page was built for publication: Material Modeling via Thermodynamics-Based Artificial Neural Networks