A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
[HTML][HTML] NN-EUCLID: Deep-learning hyperelasticity without stress data
We propose a new approach for unsupervised learning of hyperelastic constitutive laws with
physics-consistent deep neural networks. In contrast to supervised learning, which assumes …
physics-consistent deep neural networks. In contrast to supervised learning, which assumes …
Enhancing phenomenological yield functions with data: challenges and opportunities
The formulation of history-dependent material laws has been a significant research and
industrial activity in solid mechanics for over a century. A large variety of models has been …
industrial activity in solid mechanics for over a century. A large variety of models has been …
FE: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining
Herein, we present a new data-driven multiscale framework called FE ANN which is based
on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) …
on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) …
Modular machine learning-based elastoplasticity: Generalization in the context of limited data
The development of highly accurate constitutive models for materials that undergo path-
dependent processes continues to be a complex challenge in computational solid …
dependent processes continues to be a complex challenge in computational solid …
A machine learning model to predict yield surfaces from crystal plasticity simulations
We introduce a microstructurally informed machine learning model for predicting the
anisotropic yield surfaces of polycrystalline materials. A full-field, spatially resolved crystal …
anisotropic yield surfaces of polycrystalline materials. A full-field, spatially resolved crystal …
Learning hyperelastic anisotropy from data via a tensor basis neural network
Anisotropy in the mechanical response of materials with microstructure is common and yet is
difficult to assess and model. To construct accurate response models given only stress …
difficult to assess and model. To construct accurate response models given only stress …
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Data-driven constitutive modeling with neural networks has received increased interest in
recent years due to its ability to easily incorporate physical and mechanistic constraints and …
recent years due to its ability to easily incorporate physical and mechanistic constraints and …
Machine learning-enabled identification of micromechanical stress and strain hotspots predicted via dislocation density-based crystal plasticity simulations
The present work uses a full-field crystal plasticity model with a first principles-informed
dislocation density (DD) hardening law to identify the key microstructural features correlated …
dislocation density (DD) hardening law to identify the key microstructural features correlated …
Graph neural network modeling of grain-scale anisotropic elastic behavior using simulated and measured microscale data
Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-
scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain …
scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain …