A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads
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[1] Xiaohua Huang,et al. Numerical simulation of crack propagation and coalescence in marine cast iron materials using ordinary state-based peridynamics , 2022, Ocean Engineering.
[2] R. de Borst. Fracture and damage in quasi-brittle materials: A comparison of approaches , 2022, Theoretical and Applied Fracture Mechanics.
[3] H. Svendsen,et al. Thermodynamically Consistent Vapor-Liquid Equilibrium Modelling with Artificial Neural Networks , 2022, SSRN Electronic Journal.
[4] Xiao-Ping Zhou,et al. A data‐driven bond‐based peridynamic model derived from group method of data handling neural network with genetic algorithm , 2022, International Journal for Numerical Methods in Engineering.
[5] Xiaolong He,et al. Thermodynamically Consistent Machine-Learned Internal State Variable Approach for Data-Driven Modeling of Path-Dependent Materials , 2022, Computer Methods in Applied Mechanics and Engineering.
[6] Jiun-Shyan Chen,et al. A neural network‐enhanced reproducing kernel particle method for modeling strain localization , 2022, International Journal for Numerical Methods in Engineering.
[7] Xiaoping Zhou,et al. The peridynamic Drucker‐Prager plastic model with fractional order derivative for the numerical simulation of tunnel excavation , 2022, International Journal for Numerical and Analytical Methods in Geomechanics.
[8] Tongchun Li,et al. Physics-informed machine learning model for computational fracture of quasi-brittle materials without labelled data , 2022, International Journal of Mechanical Sciences.
[9] G. Karniadakis,et al. Analyses of internal structures and defects in materials using physics-informed neural networks , 2022, Science advances.
[10] R. Gao,et al. Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm , 2022, Journal of Manufacturing Systems.
[11] A. Chattopadhyay,et al. Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints , 2022, Computers & Structures.
[12] Jian Cheng Wong,et al. CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method , 2021, Computer Methods in Applied Mechanics and Engineering.
[13] Jia-Ji Wang,et al. A deep learning framework for constitutive modeling based on temporal convolutional network , 2021, J. Comput. Phys..
[14] Xiaoping Zhou,et al. A vector form conjugated-shear bond-based peridynamic model for crack initiation and propagation in linear elastic solids , 2021, Engineering Fracture Mechanics.
[15] U. Galvanetto,et al. A novel and effective way to impose boundary conditions and to mitigate the surface effect in state‐based Peridynamics , 2021, International Journal for Numerical Methods in Engineering.
[16] M. Raissi,et al. A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics , 2021, Computer Methods in Applied Mechanics and Engineering.
[17] Xu Liu,et al. Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations , 2021, 2104.06217.
[18] Diab W. Abueidda,et al. Meshless physics‐informed deep learning method for three‐dimensional solid mechanics , 2020, International Journal for Numerical Methods in Engineering.
[19] Jian-Xun Wang,et al. Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels , 2020, Physics of Fluids.
[20] P. Tahmasebi,et al. Physics informed machine learning: Seismic wave equation , 2020, Geoscience Frontiers.
[21] P. Qiao,et al. A two-dimensional ordinary state-based peridynamic model for elastic and fracture analysis , 2020, Engineering Fracture Mechanics.
[22] Ruben Juanes,et al. A nonlocal physics-informed deep learning framework using the peridynamic differential operator , 2020, ArXiv.
[23] Chris Hill,et al. DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization , 2020, Computer Methods in Applied Mechanics and Engineering.
[24] M. Yildiz,et al. An ordinary state-based peridynamic model for toughness enhancement of brittle materials through drilling stop-holes , 2020, International Journal of Mechanical Sciences.
[25] Sheng-Qi Yang,et al. Peridynamic simulation on fracture mechanical behavior of granite containing a single fissure after thermal cycling treatment , 2020 .
[26] George Em Karniadakis,et al. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations , 2020, Science.
[27] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[28] G. Shen,et al. A bond‐based peridynamic model considering effects of particle rotation and shear influence coefficient , 2019, International Journal for Numerical Methods in Engineering.
[29] Timon Rabczuk,et al. An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications , 2019, Computer Methods in Applied Mechanics and Engineering.
[30] Dan Huang,et al. A non-ordinary state-based peridynamic formulation for thermo-visco-plastic deformation and impact fracture , 2019, International Journal of Mechanical Sciences.
[31] Timon Rabczuk,et al. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture , 2019, Theoretical and Applied Fracture Mechanics.
[32] George Em Karniadakis,et al. Adaptive activation functions accelerate convergence in deep and physics-informed neural networks , 2019, J. Comput. Phys..
[33] Vito Diana,et al. A bond-based micropolar peridynamic model with shear deformability: Elasticity, failure properties and initial yield domains , 2019, International Journal of Solids and Structures.
[34] Yunteng Wang,et al. An improved coupled thermo-mechanic bond-based peridynamic model for cracking behaviors in brittle solids subjected to thermal shocks , 2019, European Journal of Mechanics - A/Solids.
[35] Charles C. Margossian,et al. A review of automatic differentiation and its efficient implementation , 2018, WIREs Data Mining Knowl. Discov..
[36] T. Rabczuk,et al. A nonlocal operator method for solving partial differential equations , 2018, 1810.02160.
[37] Xiaoping Zhou,et al. Analysis of the plastic zone near the crack tips under the uniaxial tension using ordinary state‐based peridynamics , 2018 .
[38] Erdogan Madenci,et al. Revisit of non-ordinary state-based peridynamics , 2017 .
[39] Qi-Zhi Zhu,et al. Peridynamic formulations enriched with bond rotation effects , 2017 .
[40] Yunteng Wang,et al. A 3-D conjugated bond-pair-based peridynamic formulation for initiation and propagation of cracks in brittle solids , 2017 .
[41] George E. Karniadakis,et al. Hidden physics models: Machine learning of nonlinear partial differential equations , 2017, J. Comput. Phys..
[42] Mi G. Chorzepa,et al. Higher-order approximation to suppress the zero-energy mode in non-ordinary state-based peridynamics , 2017 .
[43] T. Anderson,et al. Fracture mechanics - Fundamentals and applications , 2017 .
[44] Gilles Lubineau,et al. A morphing approach to couple state-based peridynamics with classical continuum mechanics , 2016 .
[45] Florin Bobaru,et al. Selecting the kernel in a peridynamic formulation: A study for transient heat diffusion , 2015, Comput. Phys. Commun..
[46] Timon Rabczuk,et al. Dual‐horizon peridynamics , 2015, 1506.05146.
[47] F. V. Antunes,et al. A review on 3D-FE adaptive remeshing techniques for crack growth modelling , 2015 .
[48] Barak A. Pearlmutter,et al. Automatic differentiation in machine learning: a survey , 2015, J. Mach. Learn. Res..
[49] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[50] Pablo Seleson. Improved one-point quadrature algorithms for two-dimensional peridynamic models based on analytical calculations , 2014 .
[51] Justin Schwartz,et al. A two‐dimensional ordinary, state‐based peridynamic model for linearly elastic solids , 2014 .
[52] Philippe H. Geubelle,et al. Non-ordinary state-based peridynamic analysis of stationary crack problems , 2014 .
[53] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[54] Steven J. Plimpton,et al. Implementing peridynamics within a molecular dynamics code , 2007, Comput. Phys. Commun..
[55] S. Silling,et al. A meshfree method based on the peridynamic model of solid mechanics , 2005 .
[56] G. Karniadakis,et al. nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling. , 2021, Soft matter.
[57] P. C. Paris,et al. Stress Analysis of Cracks , 1965 .
[58] Moataz O. Abu-Al-Saud,et al. Prediction of porous media fluid flow using physics informed neural networks , 2022 .