Physics-informed neural network frameworks for crack simulation based on minimized peridynamic potential energy

DOI10.1016/j.cma.2023.116430MaRDI QIDQ6153887

Zhenwei Cai, Han Dong, Wei-Zhe Wang, Ying-Zheng Liu, Luyuan Ning

Publication date: 14 February 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

zbMATH Keywords

transfer learningphysics-informed neural networkscrack initiation and propagationbond-based peridynamic

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Statistical mechanics, structure of matter (82-XX)

Cites Work

• Finite element method - The natural approach
• Strong discontinuity embedded approach with standard SOS formulation: element formulation, energy-based crack-tracking strategy, and validations
• Reformulation of elasticity theory for discontinuities and long-range forces
• A generalized bond-based peridynamic model for quasi-brittle materials enriched with bond tension-rotation-shear coupling effects
• The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
• Physics informed neural networks for continuum micromechanics
• A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches
• Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
• An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
• Computationally efficient and effective peridynamic model for cracks and fractures in homogeneous and heterogeneous materials
• A peridynamic-informed neural network for continuum elastic displacement characterization
• Application of a constitutive model for concrete to the analysis of a precast segmental tunnel lining
• Peridynamics boundary condition treatments via the pseudo-layer enrichment method and variable horizon approach
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
• Multi-Scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
• A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics
• Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology

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