81 citations · 87 across the 10 of their papers we have counts for
4 papers · 1 filter
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…