A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380)

scientific article; zbMATH DE number 7487692

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English	A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials	scientific article; zbMATH DE number 7487692

Statements

instance of

scholarly article

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title

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (English)

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George Em Karniadakis

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published in

Computer Methods in Applied Mechanics and Engineering

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publication date

11 March 2022

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full work available at URL

https://arxiv.org/abs/2108.06905

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zbMATH Keywords

deeponet

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variational energy

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physics-informed learning

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phase-field

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brittle fracture

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surrogate modeling

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cites work

DeepONet

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A phase-field description of dynamic brittle fracture

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A higher-order phase-field model for brittle fracture: formulation and analysis within the isogeometric analysis framework

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On the well-posedness of the linear peridynamic model and its convergence towards the Navier equation of linear elasticity

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Reformulation of elasticity theory for discontinuities and long-range forces

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A partitioned coupling framework for peridynamics and classical theory: analysis and simulations

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A nonlocal physics-informed deep learning framework using the peridynamic differential operator

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An asymptotically compatible meshfree quadrature rule for nonlocal problems with applications to peridynamics

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An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture

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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

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Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models

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Data-driven learning of nonlocal physics from high-fidelity synthetic data

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A method for interpolating on manifolds structural dynamics reduced-order models

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

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Non-invasive inference of thrombus material properties with physics-informed neural networks

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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Solving ill-posed inverse problems using iterative deep neural networks

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Prediction of aerodynamic flow fields using convolutional neural networks

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Adaptive fourth-order phase field analysis for brittle fracture

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VI. The phenomena of rupture and flow in solids

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Revisiting brittle fracture as an energy minimization problem

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Numerical experiments in revisited brittle fracture

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A phase field model for rate-independent crack propagation: robust algorithmic implementation based on operator splits

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Q4873722

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DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks

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Second-order phase-field formulations for anisotropic brittle fracture

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Random heterogeneous materials. Microstructure and macroscopic properties

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describes a project that uses

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2022.114587

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Sitelinks

Mathematics(1 entry)

mardi Publication:2670380