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- W4379142783 abstract "In this talk I will present I-FENN, a novel Integrated Finite Element Neural Network framework. The objective of I-FENN is to accelerate the numerical solution of non-linear, multi-physics, computational mechanics problems. The central idea is to deploy physics-informed machine learning algorithms directly in the finite element stiffness function, to approximate state variables of interest at a glance while ensuring the convergence of nonlinear solvers such as Newton Raphson. In this work, I-FENN is implemented in the continuum damage analysis of quasi-brittle materials. A new non-local gradient damage framework which operates at the cost of a local damage approach is established. At the offline stage, the pre-trained network receives as input the deformation state of each material point and learns to predict the corresponding nonlocal strain, as well as its derivative with respect to the local strain. Then, in the online stage, the network is integrated in the element stiffness definition and its outputs are used to construct the element Jacobian and residual vector. The latter process is carried out within the nonlinear solver until convergence is achieved. Therefore, the proposed method tackles the vital drawbacks of both the local and non-local gradient method, respectively being the mesh-dependence and the additional computational cost. A series of numerical examples showcases the feasibility, computational efficiency, generalization capability and robustness of I-FENN." @default.
- W4379142783 created "2023-06-03" @default.
- W4379142783 creator A5061087099 @default.
- W4379142783 date "2023-05-31" @default.
- W4379142783 modified "2023-09-26" @default.
- W4379142783 title "Integrated Finite Element Neural Network (I-FENN) for non-local continuum damage mechanics" @default.
- W4379142783 cites W4310286275 @default.
- W4379142783 doi "https://doi.org/10.52843/cassyni.tmv2j0" @default.
- W4379142783 hasPublicationYear "2023" @default.
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