A data-driven reduced-order surrogate model for entire elastoplastic simulations applied to representative volume elements
This contribution discusses surrogate models that emulate the solution field (s) in the entire
simulation domain. The surrogate uses the most characteristic modes of the solution field (s) …
simulation domain. The surrogate uses the most characteristic modes of the solution field (s) …
[HTML][HTML] Exploring the roles of numerical simulations and machine learning in multiscale paving materials analysis: Applications, challenges, best practices
The complex structure of bituminous mixtures ranging from nanoscale binder components to
macroscale pavement performance requires a comprehensive approach to material …
macroscale pavement performance requires a comprehensive approach to material …
[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 …
[HTML][HTML] Micromechanics-based deep-learning for composites: Challenges and future perspectives
M Mirkhalaf, I Rocha - European Journal of Mechanics-A/Solids, 2024 - Elsevier
During the last few decades, industries such as aerospace and wind energy (among others)
have been remarkably influenced by the introduction of high-performance composites. One …
have been remarkably influenced by the introduction of high-performance composites. One …
[HTML][HTML] Recurrent neural networks and transfer learning for predicting elasto-plasticity in woven composites
Woven composites exhibit complex meso-scale behavior depending on meso-and micro-
structural parameters. Accurately modeling their mechanical response is challenging and …
structural parameters. Accurately modeling their mechanical response is challenging and …
[HTML][HTML] Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation
Y Yamanaka, S Matsubara, N Hirayama… - Computer Methods in …, 2023 - Elsevier
We propose a new framework for creating a surrogate model of computational
homogenization for elastoplastic composite materials that serves as a homogenized …
homogenization for elastoplastic composite materials that serves as a homogenized …
Recurrent neural networks and transfer learning for elasto-plasticity in woven composites
As a surrogate for computationally intensive meso-scale simulation of woven composites,
this article presents Recurrent Neural Network (RNN) models. Leveraging the power of …
this article presents Recurrent Neural Network (RNN) models. Leveraging the power of …
Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks
We applied physics-informed neural networks to solve the constitutive relations for
nonlinear, path-dependent material behavior. As a result, the trained network not only …
nonlinear, path-dependent material behavior. As a result, the trained network not only …
[HTML][HTML] How can machine learning be used for accurate representations and predictions of fracture nucleation in zirconium alloys with hydride populations?
Zirconium alloys are critical material components of systems subjected to harsh
environments such as high temperatures, irradiation, and corrosion. When exposed to water …
environments such as high temperatures, irradiation, and corrosion. When exposed to water …
Self-consistency Reinforced minimal Gated Recurrent Unit for surrogate modeling of history-dependent non-linear problems: Application to history-dependent …
Multi-scale simulations can be accelerated by substituting the meso-scale problem
resolution by a surrogate trained from off-line simulations. In the context of history …
resolution by a surrogate trained from off-line simulations. In the context of history …