81 citations · 86 across the 5 of their papers we have counts for
6 papers
On architectural choices for interpretability and thermodynamic consistency in Physically Recurrent Neural Networks in the low-data regime
M. A. Maia, K. A. Meyer, A. M. C. M. van Gils +2
In this paper, we unravel the effect of different decoder architectures on the interpretability of the latent space of the Physically Recurrent Neural Network. Particular emphasis…
Multiscale Analysis of Woven Composites Using Hierarchical Physically Recurrent Neural Networks
Ehsan Ghane, Marina A. Maia, Iuri B. C. M. Rocha +2
Multiscale homogenization of woven composites requires detailed micromechanical evaluations, leading to high computational costs. Data-driven surrogate models based on neural netwo…
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…
Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework
M. A. Maia, I. B. C. M. Rocha, D. Kovačević +1
In this work, a hybrid physics-based data-driven surrogate model for the microscale analysis of heterogeneous material is investigated. The proposed model benefits from the physics…
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…