81 citations · 121 across the 10 of their papers we have counts for
5 papers · 1 filter
The Bayesian Finite Element Method in Inverse Problems: a Critical Comparison between Probabilistic Models for Discretization Error
Anne Poot, Iuri Rocha, Pierre Kerfriden +1
When using the finite element method (FEM) in inverse problems, its discretization error can produce parameter estimates that are inaccurate and overconfident. The Bayesian finite…
Mixing Data-Driven and Physics-Based Constitutive Models using Uncertainty-Driven Phase Fields
J. Storm, W. Sun, I. B. C. M. Rocha +1
There is a high interest in accelerating multiscale models using data-driven surrogate modeling techniques. Creating a large training dataset encompassing all relevant load scenari…
A Bayesian Approach to Modeling Finite Element Discretization Error
Anne Poot, Pierre Kerfriden, Iuri Rocha +1
In this work, the uncertainty associated with the finite element discretization error is modeled following the Bayesian paradigm. First, a continuous formulation is derived, where…
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…