A virtual sample generation algorithm supporting machine learning with a small-sample dataset: A case study for rubber materials

NNI‐SMOTE‐XGBoost: A Novel Small Sample Analysis Method for Properties Prediction of Polymer Materials

(2021) Dazi Li et al.   MACROMOLECULAR THEORY AND SIMULATIONS

Deep learning framework for material design space exploration using active transfer learning and data augmentation

(2021) Yongtae Kim et al.   npj Computational Materials

Auto-encoder-based generative models for data augmentation on regression problems

(2019) Hiroshi Ohno   SOFT COMPUTING

Deep materials informatics: Applications of deep learning in materials science

(2019) Ankit Agrawal et al.   MRS Communications

Machine learning and artificial neural network accelerated computational discoveries in materials science

(2019) Yang Hong et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

An evolutionary classifier for steel surface defects with small sample set

(2017) Mang Xiao et al.   EURASIP Journal on Image and Video Processing

A Study on Microstructural Parameters for the Characterization of Granular Porous Ceramics Using a Combination of Stochastic and Mechanical Modeling

(2017) Matthias Kulosa et al.   International Journal of Applied Mechanics

A fast and reliable overset unstructured grids approach

(2013) Zhong-Liang Kang et al.   ACTA MECHANICA SINICA

Application of artificial neural network in prediction of abrasion of rubber composites

(2013) Bin Wang et al.   MATERIALS & DESIGN

LARGE-SCALE COMPUTATIONS OF EFFECTIVE ELASTIC PROPERTIES OF RUBBER WITH CARBON BLACK FILLERS

(2011) Aurelie Jean et al.   International Journal for Multiscale Computational Engineering

A novel virtual sample generation method based on Gaussian distribution

(2011) Jing Yang et al.   KNOWLEDGE-BASED SYSTEMS

Model selection strategies for identifying most relevant covariates in homoscedastic linear models

(2009) Aleksey Min et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS