Accelerating finite element analysis using machine learning

We study the acceleration of the finite element method (FEM) simulations using machine learning (ML) models. Specifically, we replace computationally expensive (parts of) FEM models with efficient ML surrogates. We develop three methods to speed up FEM simulations. The primary difference between these models is their degree of intrusion into the...

Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network

FE² multiscale simulations of history-dependent materials are accelerated by means of a recurrent neural network (RNN) surrogate for the history-dependent micro level response. We propose a simple strategy to efficiently collect stress–strain data from the micro model, and we modify the RNN model such that it resembles a nonlinear...

POD-DEIM model order reduction for strain softening viscoplasticity

We demonstrate a Model Order Reduction technique for a system of nonlinear equations arising from the Finite Element Method (FEM) discretization of the three-dimensional quasistatic equilibrium equation equipped with a Perzyna viscoplasticity constitutive model. The procedure employs the Proper Orthogonal Decomposition-Galerkin (POD-G) in...