A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
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Publication:2670380
DOI10.1016/j.cma.2022.114587OpenAlexW4210423197MaRDI QIDQ2670380
Yue Yu, Somdatta Goswami, Minglang Yin, George Em. Karniadakis
Publication date: 11 March 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.06905
Brittle fracture (74R10) Numerical methods for partial differential equations, boundary value problems (65N99)
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Uses Software
Cites Work
- Unnamed Item
- A higher-order phase-field model for brittle fracture: formulation and analysis within the isogeometric analysis framework
- A phase field model for rate-independent crack propagation: robust algorithmic implementation based on operator splits
- A phase-field description of dynamic brittle fracture
- DeepONet
- Numerical experiments in revisited brittle fracture
- Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Revisiting brittle fracture as an energy minimization problem
- Reformulation of elasticity theory for discontinuities and long-range forces
- A partitioned coupling framework for peridynamics and classical theory: analysis and simulations
- An asymptotically compatible meshfree quadrature rule for nonlocal problems with applications to peridynamics
- Data-driven learning of nonlocal physics from high-fidelity synthetic data
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture
- Non-invasive inference of thrombus material properties with physics-informed neural networks
- Second-order phase-field formulations for anisotropic brittle fracture
- DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks
- Adaptive fourth-order phase field analysis for brittle fracture
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Prediction of aerodynamic flow fields using convolutional neural networks
- On the well-posedness of the linear peridynamic model and its convergence towards the Navier equation of linear elasticity
- A method for interpolating on manifolds structural dynamics reduced-order models
- Solving ill-posed inverse problems using iterative deep neural networks
- VI. The phenomena of rupture and flow in solids
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
- Random heterogeneous materials. Microstructure and macroscopic properties
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