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 …
Advanced ocean wave energy harvesting: current progress and future trends
With a transition towards clean and low-carbon renewable energy, against the backdrop of
the fossil-energy crisis and rising pollution, ocean energy has been proposed as a …
the fossil-energy crisis and rising pollution, ocean energy has been proposed as a …
A FDEM approach to study mechanical and fracturing responses of geo-materials with high inclusion contents using a novel reconstruction strategy
Y Lin, J Ma, Z Lai, L Huang, M Lei - Engineering Fracture Mechanics, 2023 - Elsevier
In this paper, a detailed FDEM approach to simulate the mechanical and fracturing
responses of heterogeneous geomaterials with irregular inclusions is systematically …
responses of heterogeneous geomaterials with irregular inclusions is systematically …
Modeling structure-property relationships with convolutional neural networks: Yield surface prediction based on microstructure images
The use of micromechanics in conjunction with homogenization theory allows for the
prediction of the effective mechanical properties of materials based on microstructural …
prediction of the effective mechanical properties of materials based on microstructural …
[HTML][HTML] From CP-FFT to CP-RNN: Recurrent neural network surrogate model of crystal plasticity
Abstract Recurrent Neural Network (RNN) based surrogate models constitute an emerging
class of reduced order models of history-dependent material behavior. Recently, the authors …
class of reduced order models of history-dependent material behavior. Recently, the authors …
[HTML][HTML] Deep active learning for constitutive modelling of granular materials: From representative volume elements to implicit finite element modelling
Constitutive relation remains one of the most important, yet fundamental challenges in the
study of granular materials. Instead of using closed-form phenomenological models or …
study of granular materials. Instead of using closed-form phenomenological models or …
Determination of ductile fracture properties of 16MND5 steels under varying constraint levels using machine learning methods
The current paper presents a machine learning method based on artificial neural network
(ANN) model for the determination of ductile fracture properties of 16MND5 bainitic forging …
(ANN) model for the determination of ductile fracture properties of 16MND5 bainitic forging …
A deep learning energy-based method for classical elastoplasticity
The deep energy method (DEM) has been used to solve the elastic deformation of structures
with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on …
with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on …
A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials
Data-driven methods have emerged as a promising framework for material constitutive
modeling. However, traditional data-driven models are hindered by limitations arising from a …
modeling. However, traditional data-driven models are hindered by limitations arising from a …
Data-driven multiscale modelling of granular materials via knowledge transfer and sharing
Abstract Machine learning approaches have found immense potential to revolutionise the
constitutive modelling of granular materials. However, data scarcity poses a significant …
constitutive modelling of granular materials. However, data scarcity poses a significant …