A nonlocal physics-informed deep learning framework using the peridynamic differential operator
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Publication:2237731
DOI10.1016/j.cma.2021.114012zbMath1502.65172arXiv2006.00446OpenAlexW3031879091WikidataQ111462157 ScholiaQ111462157MaRDI QIDQ2237731
Ehsan Haghighat, Ali C. Bekar, Ruben Juanes, Erdogan Madenci
Publication date: 28 October 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2006.00446
Artificial neural networks and deep learning (68T07) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99) Peridynamics (74A70)
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Uses Software
Cites Work
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- Convergence of peridynamics to classical elasticity theory
- Computational inelasticity
- Reformulation of elasticity theory for discontinuities and long-range forces
- PPINN: parareal physics-informed neural network for time-dependent PDEs
- SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Peridynamic states and constitutive modeling
- Data-driven computational mechanics
- Peridynamic differential operator and its applications
- Peridynamic Theory and Its Applications
- Data-Driven Science and Engineering
- Solving high-dimensional partial differential equations using deep learning
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- Data-driven discovery of coordinates and governing equations
- Learning data-driven discretizations for partial differential equations
- Numerical solution of linear and nonlinear partial differential equations using the peridynamic differential operator
- Data-Driven Identification of Parametric Partial Differential Equations
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