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 …
Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network
(HiDeNN) is proposed to solve challenging computational science and engineering …
(HiDeNN) is proposed to solve challenging computational science and engineering …
Discovering plasticity models without stress data
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 …
call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we …
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 Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features
Multiscale computational modelling is challenging due to the high computational cost of
direct numerical simulation by finite elements. To address this issue, concurrent multiscale …
direct numerical simulation by finite elements. To address this issue, concurrent multiscale …
A mechanistic-based data-driven approach for general friction modeling in complex mechanical system
The effect of friction is widespread around us, and most important projects must consider the
friction effect. To better depict the dynamic characteristics of multibody systems with friction …
friction effect. To better depict the dynamic characteristics of multibody systems with friction …
A machine learning-based multi-scale computational framework for granular materials
With the development of experimental measurement technology and high-fidelity numerical
simulations of granular materials, empirical-based classical constitutive models may not be …
simulations of granular materials, empirical-based classical constitutive models may not be …
Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks
A mechanistically informed data-driven approach is proposed to simulate the complex
plastic behavior of microstructured/homogenized solids subjected to cyclic loading …
plastic behavior of microstructured/homogenized solids subjected to cyclic loading …
A database construction method for data-driven computational mechanics of composites
A new method combining computational homogenization and the Artificial Neural Network
(ANN) is proposed to construct elastoplastic composites database efficiently for data-driven …
(ANN) is proposed to construct elastoplastic composites database efficiently for data-driven …
A mechanics-informed machine learning approach for modeling the elastoplastic behavior of fiber-reinforced composites
Z Li, X Li, Y Chen, C Zhang - Composite Structures, 2023 - Elsevier
When machine learning (ML) techniques are used to predict the elastoplastic behavior of a
fiber-reinforced composite, a large training database is typically required due to the …
fiber-reinforced composite, a large training database is typically required due to the …