81 citations · 87 across the 10 of their papers we have counts for
4 papers · 1 filter
Surrogate-based multiscale analysis of experiments on thermoplastic composites under off-axis loading
M. A. Maia, I. B. C. M. Rocha, D. Kovačević +1
In this paper, we present a surrogate-based multiscale approach to model constant strain-rate and creep experiments on unidirectional thermoplastic composites under off-axis loadin…
Physically Recurrent Neural Networks for Computational Homogenization of Composite Materials with Microscale Debonding
N. Kovács, M. A. Maia, I. B. C. M. Rocha +3
The growing use of composite materials in engineering applications has accelerated the demand for computational methods to accurately predict their complex behavior. Multiscale mod…
Machine learning of evolving physics-based material models for multiscale solid mechanics
I. B. C. M. Rocha, P. Kerfriden, F. P. van der Meer
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 behavior. W…
Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate
M. A. Maia, I. B. C. M. Rocha, P. Kerfriden +1
Driven by the need to accelerate numerical simulations, the use of machine learning techniques is rapidly growing in the field of computational solid mechanics. Their application i…