81 citations · 121 across the 10 of their papers we have counts for
12 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…
Microstructure-Conditioned Surrogate Models for Graded Multiscale Optimization of Mycelium Composites
J. Storm, I. B. C. M. Rocha, S. Schyck +2
Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior. Optimizing these materia…
A Shell-to-Shell Cohesive Line Element for Efficient Modeling of Interfacial Cracking in Overmolded Stiffened Panels
Sérgio G. F. Cordeiro, Boyang Chen, Frans P. van der Meer
The growing use of thermoplastics in lightweight structures requires efficient numerical methods to predict debonding in overmolded parts. In this work, a novel structural cohesive…
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…
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…
Modeling of progressive high-cycle fatigue in composite laminates accounting for local stress ratios
P. Hofman, F. P. van der Meer, L. J. Sluys
A numerical framework for simulating progressive failure under high-cycle fatigue loading is validated against experiments of composite quasi-isotropic open-hole laminates. Transve…
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…
A numerical framework for simulating progressive failure in composite laminates under high-cycle fatigue loading
Pieter Hofman, Frans Paul van der Meer, Lambertus Johannes Sluys
In this work, a recently proposed high-cycle fatigue cohesive zone model, which covers crack initiation and propagation with limited input parameters, is embedded in a robust and e…
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…