26 research outputs found
Hysteretic Behavior Simulation Based on Pyramid Neural Network:Principle, Network Architecture, Case Study and Explanation
An accurate and efficient simulation of the hysteretic behavior of materials
and components is essential for structural analysis. The surrogate model based
on neural networks shows significant potential in balancing efficiency and
accuracy. However, its serial information flow and prediction based on
single-level features adversely affect the network performance. Therefore, a
weighted stacked pyramid neural network architecture is proposed herein. This
network establishes a pyramid architecture by introducing multi-level shortcuts
to integrate features directly in the output module. In addition, a weighted
stacked strategy is proposed to enhance the conventional feature fusion method.
Subsequently, the redesigned architectures are compared with other commonly
used network architectures. Results show that the redesigned architectures
outperform the alternatives in 87.5% of cases. Meanwhile, the long and
short-term memory abilities of different basic network architectures are
analyzed through a specially designed experiment, which could provide valuable
suggestions for network selection.Comment: 41 pages, 14 figure
Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems
Accurate structural response prediction forms a main driver for structural
health monitoring and control applications. This often requires the proposed
model to adequately capture the underlying dynamics of complex structural
systems. In this work, we utilize a learnable Extended Kalman Filter (EKF),
named the Neural Extended Kalman Filter (Neural EKF) throughout this paper, for
learning the latent evolution dynamics of complex physical systems. The Neural
EKF is a generalized version of the conventional EKF, where the modeling of
process dynamics and sensory observations can be parameterized by neural
networks, therefore learned by end-to-end training. The method is implemented
under the variational inference framework with the EKF conducting inference
from sensing measurements. Typically, conventional variational inference models
are parameterized by neural networks independent of the latent dynamics models.
This characteristic makes the inference and reconstruction accuracy weakly
based on the dynamics models and renders the associated training inadequate. We
here show how the structure imposed by the Neural EKF is beneficial to the
learning process. We demonstrate the efficacy of the framework on both
simulated and real-world monitoring datasets, with the results indicating
significant predictive capabilities of the proposed scheme.Comment: This manuscript has been submitted to an international journal for
revie
On the use of graph neural networks and shape-function-based gradient computation in the deep energy method
A graph neural network (GCN) is employed in the deep energy method (DEM)
model to solve the momentum balance equation in 3D for the deformation of
linear elastic and hyperelastic materials due to its ability to handle
irregular domains over the traditional DEM method based on a multilayer
perceptron (MLP) network. Its accuracy and solution time are compared to the
DEM model based on a MLP network. We demonstrate that the GCN-based model
delivers similar accuracy while having a shorter run time through numerical
examples. Two different spatial gradient computation techniques, one based on
automatic differentiation (AD) and the other based on shape function (SF)
gradients, are also accessed. We provide a simple example to demonstrate the
strain localization instability associated with the AD-based gradient
computation and show that the instability exists in more general cases by four
numerical examples. The SF-based gradient computation is shown to be more
robust and delivers an accurate solution even at severe deformations.
Therefore, the combination of the GCN-based DEM model and SF-based gradient
computation is potentially a promising candidate for solving problems involving
severe material and geometric nonlinearities
From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems
Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section