[HTML][HTML] A review of artificial neural networks in the constitutive modeling of composite materials
Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …
to the widespread digital data, growing computing power, and advanced algorithms. The …
[HTML][HTML] On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning
Concurrent multiscale finite element analysis (FE 2) is a powerful approach for high-fidelity
modeling of materials for which a suitable macroscopic constitutive model is not available …
modeling of materials for which a suitable macroscopic constitutive model is not available …
[HTML][HTML] Machine learning of evolving physics-based material models for multiscale solid mechanics
In this work we present a hybrid physics-based and data-driven learning approach to
construct surrogate models for concurrent multiscale simulations of complex material …
construct surrogate models for concurrent multiscale simulations of complex material …
Data-driven models for crashworthiness optimisation: intrusive and non-intrusive model order reduction techniques
C Czech, M Lesjak, C Bach, F Duddeck - Structural and Multidisciplinary …, 2022 - Springer
To enable multi-query analyses, such as optimisations of large-scale crashworthiness
problems, a numerically efficient model is crucial for the development process. Therefore …
problems, a numerically efficient model is crucial for the development process. Therefore …
A reduced order model based on adaptive proper orthogonal decomposition incorporated with modal coefficient learning for digital twin in process industry
X Zhu, Y Ji - Journal of Manufacturing Processes, 2023 - Elsevier
The digital twin (DT) technology provides a viable and promising direction for improving the
level of the production status monitoring and the overall product quality in various fields …
level of the production status monitoring and the overall product quality in various fields …
A local basis approximation approach for nonlinear parametric model order reduction
The efficient condition assessment of engineered systems requires the coupling of high
fidelity models with data extracted from the state of the system 'as-is'. In enabling this task …
fidelity models with data extracted from the state of the system 'as-is'. In enabling this task …
Cell division in deep material networks applied to multiscale strain localization modeling
Z Liu - Computer Methods in Applied Mechanics and …, 2021 - Elsevier
Despite the increasing importance of strain localization modeling (eg, failure analysis) in
computer-aided engineering, there is a lack of effective approaches to capturing relevant …
computer-aided engineering, there is a lack of effective approaches to capturing relevant …
A monolithic hyper ROM FE2 method with clustered training at finite deformations
The usage of numerical homogenization to obtain structure–property relations by applying
the finite element method at both the micro-and macroscale has gained much interest in the …
the finite element method at both the micro-and macroscale has gained much interest in the …
Jive: An open source, research-oriented C++ library for solving partial differential equations
A majority of physical models are written as partial differential equations. For most of these
equations, analytical solutions cannot be obtained and they can be solved only numerically …
equations, analytical solutions cannot be obtained and they can be solved only numerically …
Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction
In this work, a novel approach is introduced for accelerating the solution of structural
dynamics problems in the presence of localised phenomena, such as cracks. For this …
dynamics problems in the presence of localised phenomena, such as cracks. For this …