Integrated Finite Element Neural Network (I-FENN) for non-local continuum damage mechanics
نویسندگان
چکیده
We present a new Integrated Finite Element Neural Network framework (I-FENN), with the objective to accelerate numerical solution of nonlinear computational mechanics problems. leverage swift predictive capability neural networks (NNs) and we embed them inside finite element stiffness function, compute element-level state variables their derivatives within nonlinear, iterative solution. This process is conducted jointly conventional methods that involve shape functions: NN receives input data resembles material point deformation its output used construct field such as Jacobian matrix residual vector. Here introduce I-FENN continuum damage analysis quasi-brittle materials, establish non-local gradient-based which operates at cost local approach. First, develop physics informed network (PINN) resemble gradient model then train offline. The learns predict equivalent strain each point, well derivative respect strain. Then, PINN integrated in definition conducts transformation, whereas two outputs are carried out solver, until convergence achieved. resulting method bears approach, but ensures mesh-independent results diffused profile. As result, proposed tackles vital drawbacks both method, respectively being mesh-dependence additional cost. showcase through series examples efficiency generalization context damage, discuss future outlook. training code associated files have been made publicly available online, enable reproducibility our results.
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ژورنال
عنوان ژورنال: Computer Methods in Applied Mechanics and Engineering
سال: 2023
ISSN: ['0045-7825', '1879-2138']
DOI: https://doi.org/10.1016/j.cma.2022.115766