81 citations · 87 across the 10 of their papers we have counts for
11 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…
Piecewise Deterministic Markov Processes for Bayesian Inference of PDE Coefficients
Leon Riccius, Iuri B. C. M. Rocha, Joris Bierkens +2
We develop a general framework for piecewise deterministic Markov process (PDMP) samplers that enables efficient Bayesian inference in non-linear inverse problems with expensive li…
Uncertainty Quantification in Multiscale Modeling of Polymer Composite Materials Using Physically Recurrent Neural Networks
N. Kovács, I. B. C. M. Rocha, F. P. van der Meer +2
This study investigates whether Physically Recurrent Neural Networks (PRNNs), a recent surrogate model for heterogeneous materials, trained on a micromodel with fixed material para…
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…
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties
Leon Riccius, Iuri B. C. M. Rocha, Joris Bierkens +2
Recent advancements in Markov chain Monte Carlo (MCMC) sampling and surrogate modelling have significantly enhanced the feasibility of Bayesian analysis across engineering fields.…
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…
A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations
J. Storm, I. B. C. M. Rocha, F. P. van der Meer
Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computation…
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…