Discovering plasticity models without stress data

M Flaschel, S Kumar, L De Lorenzis - npj Computational Materials, 2022 - nature.com
We propose an approach for data-driven automated discovery of material laws, which we
call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we …

A comparative investigation using machine learning methods for concrete compressive strength estimation

K Güçlüer, A Özbeyaz, S Göymen… - Materials Today …, 2021 - Elsevier
Concrete compressive strength plays an important role in determining the mechanical
properties of concrete. The determination of concrete compressive strength requires lengthy …

[HTML][HTML] Void growth in ductile materials with realistic porous microstructures

AR Vishnu, G Vadillo, JA Rodríguez-Martínez - International Journal of …, 2023 - Elsevier
In this paper, we have investigated void growth in von Mises materials which contain
realistic porous microstructures. For that purpose, we have performed finite element …

Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations

JN Fuhg, L van Wees, M Obstalecki, P Shade… - Materialia, 2022 - Elsevier
The influence of the microstructure of a polycrystalline material on its macroscopic
deformation response is still one of the major problems in materials engineering. For …

Estimation of constituent properties of concrete materials with an artificial neural network based method

J Xue, JF Shao, N Burlion - Cement and Concrete Research, 2021 - Elsevier
Multi-scale models are developed for heterogeneous concrete materials to estimate their
macroscopic mechanical properties in terms of micro-structural data. One crucial challenge …

[HTML][HTML] Data-driven modelling of the multiaxial yield behaviour of nanoporous metals

L Dyckhoff, N Huber - International journal of mechanical sciences, 2023 - Elsevier
Nanoporous metals, built out of complex ligament networks, can be produced with an
additional level of hierarchy. The resulting complexity of the structure makes modelling of the …

[HTML][HTML] FE-LSTM: A hybrid approach to accelerate multiscale simulations of architectured materials using Recurrent Neural Networks and Finite Element Analysis

A Danoun, E Prulière, Y Chemisky - Computer Methods in Applied …, 2024 - Elsevier
In the present work, a novel modeling strategy to accelerate multi-scale simulations of
heterogeneous materials using deep neural networks is developed. This approach, called …

A data-driven yield criterion for porous ductile single crystals containing spherical voids via physics-informed neural networks

L Wu, J Fu, H Sui, X Wang, B Tao… - … of the Royal …, 2023 - royalsocietypublishing.org
Yield criteria for porous material have been widely used to model the decrease of yield
strength caused by porosity during ductile failure which deserves long-term efforts in …

[HTML][HTML] A simple machine learning-based framework for faster multi-scale simulations of path-independent materials at large strains

AMC Carneiro, AFC Alves, RPC Coelho… - Finite Elements in …, 2023 - Elsevier
Coupled multi-scale finite element analyses have gained traction over the last years due to
the increasing available computational resources. Nevertheless, in the pursuit of accurate …

Automated Discovery of Material Models in Continuum Solid Mechanics

M Flaschel - 2023 - research-collection.ethz.ch
The mathematical description of the mechanical behavior of solid materials at the continuum
scale is one of the oldest and most challenging tasks in solid mechanics and material …