1 citations · 2 across the 6 of their papers we have counts for
8 papers
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…