Convolution hierarchical deep-learning neural network (c-hidenn) with graphics processing unit (gpu) acceleration
Abstract We propose the Convolution Hierarchical Deep-learning Neural Network (C-
HiDeNN) that can be tuned to have superior accuracy, higher smoothness, and faster …
HiDeNN) that can be tuned to have superior accuracy, higher smoothness, and faster …
An overview on uncertainty quantification and probabilistic learning on manifolds in multiscale mechanics of materials
C Soize - Mathematics and Mechanics of Complex Systems, 2023 - msp.org
An overview of the author's works, many of which were carried out in collaboration, is
presented. The first part concerns the quantification of uncertainties for complex engineering …
presented. The first part concerns the quantification of uncertainties for complex engineering …
Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation
In this study, a deep learning framework for multiscale finite element analysis (FE 2) is
proposed. To overcome the inefficiency of the concurrent classical FE 2 method induced by …
proposed. To overcome the inefficiency of the concurrent classical FE 2 method induced by …
[HTML][HTML] Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena
This article introduces adaptivity in Clustering-based Reduced Order Models (ACROMs).
The strategy is demonstrated for a particular CROM called Self-Consistent Clustering …
The strategy is demonstrated for a particular CROM called Self-Consistent Clustering …
Concurrent multiscale virtual testing for 2D woven composite structures: a pathway towards composites design and structure optimization
Virtual testing is a powerful tool to characterize the mechanical behavior of materials owing
to its excellent predictive ability. However, virtual testing for composite structures remains …
to its excellent predictive ability. However, virtual testing for composite structures remains …
Concurrent n-scale modeling for non-orthogonal woven composite
Concurrent analysis of composite materials can provide the interaction among scales for
better composite design, analysis, and performance prediction. A data-driven concurrent n …
better composite design, analysis, and performance prediction. A data-driven concurrent n …
Model-free data-driven identification algorithm enhanced by local manifold learning
Reliable and consistent material data identification is essential to the data-driven
computational mechanics paradigm. This paper presents a generalized data-driven …
computational mechanics paradigm. This paper presents a generalized data-driven …
Multiscale analysis of nonlinear systems using a hierarchy of deep neural networks
The application of multiscale methods that are based on computational homogenization,
such as the well established FE 2, remains in most cases a computationally challenging …
such as the well established FE 2, remains in most cases a computationally challenging …
Probabilistic learning inference of boundary value problem with uncertainties based on Kullback–Leibler divergence under implicit constraints
C Soize - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
This paper deals with a probabilistic learning inference that allows for integrating data
(target set) into predictive models for which the target set is constituted of statistical moments …
(target set) into predictive models for which the target set is constituted of statistical moments …
Cyclic softening in nonlocal shells—A data-driven graph-gradient plasticity approach
Cyclic softening is a plastic mechanism that affects the strength of various structural
materials while in operation, including that of many advanced alloys and modern …
materials while in operation, including that of many advanced alloys and modern …