A review of the multiscale mechanics of silicon electrodes in high-capacity lithium-ion batteries
Over the past decade, there has been a significant advancement in understanding the
mechanics of silicon (Si) electrodes in lithium (Li)-ion batteries. Much of this interest in Si …
mechanics of silicon (Si) electrodes in lithium (Li)-ion batteries. Much of this interest in Si …
An overview on uncertainty quantification and probabilistic learning on manifolds in multiscale mechanics of materials
C Soize - Mathematics and Mechanics of Complex Systems, 2023 - msp.org
An overview of the author's works, many of which were carried out in collaboration, is
presented. The first part concerns the quantification of uncertainties for complex engineering …
presented. The first part concerns the quantification of uncertainties for complex engineering …
Investigation and optimization of the C-ANN structure in predicting the compressive strength of foamed concrete
DV Dao, HB Ly, HLT Vu, TT Le, BT Pham - Materials, 2020 - mdpi.com
Development of Foamed Concrete (FC) and incessant increases in fabrication technology
have paved the way for many promising civil engineering applications. Nevertheless, the …
have paved the way for many promising civil engineering applications. Nevertheless, the …
Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach
Polymer-assisted flocculation-dewatering of mineral processing tailings (MPT) is crucial for
its environmental disposal. To reduce the number of laboratory experiments, this study …
its environmental disposal. To reduce the number of laboratory experiments, this study …
Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems
We consider a high-dimensional nonlinear computational model of a dynamical system,
parameterized by a vector-valued control parameter, in the presence of uncertainties …
parameterized by a vector-valued control parameter, in the presence of uncertainties …
Extreme learning machine based prediction of soil shear strength: a sensitivity analysis using Monte Carlo simulations and feature backward elimination
Machine Learning (ML) has been applied widely in solving a lot of real-world problems.
However, this approach is very sensitive to the selection of input variables for modeling and …
However, this approach is very sensitive to the selection of input variables for modeling and …
Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold
DG Giovanis, MD Shields - Computer Methods in Applied Mechanics and …, 2020 - Elsevier
This paper introduces a surrogate modeling scheme based on Grassmannian manifold
learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The …
learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The …
Hybrid machine-learning-assisted stochastic nano-indentation behaviour of twisted bilayer graphene
We present herein a polynomial chaos-Kriging (PC-Kriging)-based molecular dynamics
(MD) simulation framework of twisted bilayer graphene (tBLG) structures to investigate the …
(MD) simulation framework of twisted bilayer graphene (tBLG) structures to investigate the …
Optimization of neural network parameters in improvement of particulate matter concentration prediction of open-pit mining
The prediction of particulate matter (PM) concentration around open-pit mining is crucial for
its control. To achieve this, machine learning (ML) techniques have been attempted in PM …
its control. To achieve this, machine learning (ML) techniques have been attempted in PM …
Nematic liquid crystalline elastomers are aeolotropic materials
Continuum models describing ideal nematic solids are widely used in theoretical studies of
liquid crystal elastomers. However, experiments on nematic elastomers show a type of …
liquid crystal elastomers. However, experiments on nematic elastomers show a type of …