A deep learning framework for constitutive modeling based on temporal convolutional network

DOI10.1016/j.jcp.2021.110784OpenAlexW3205903219MaRDI QIDQ2136467

Yanyan Li

Publication date: 10 May 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110784

zbMATH Keywords

constitutive modellong-term memorydeep learningconvolutional neural networktemporal convolutional networkphysics-informed machine learning (PIML)

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Plastic materials, materials of stress-rate and internal-variable type (74Cxx) Generalities, axiomatics, foundations of continuum mechanics of solids (74Axx)

Related Items (3)

A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads ⋮ Incremental neural controlled differential equations for modeling of path-dependent material behavior ⋮ Exact Dirichlet boundary physics-informed neural network EPINN for solid mechanics

Uses Software

• OpenSeesPy
• Adam
• ByteNet
• WaveNet
• F3DAM

Cites Work

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• Three dimensional combined fracture-plastic material model for concrete
• Computational inelasticity
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• Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
• A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model
• Hierarchical deep-learning neural networks: finite elements and beyond
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• An energy release rate-based plastic-damage model for concrete

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