Application of Machine Learning to Predict the Multiaxial Strain-Sensing Response of CNT-polymer Composites

Carbon- United Kingdom

doi 10.1016/j.carbon.2019.02.001

Full Text

Abstract

Available in

Date

Authors

Publisher

Elsevier BV

Related search

Modeling Electrical Percolation to Optimize the Electromechanical Properties of CNT/Polymer Composites in Highly Stretchable Fiber Strain Sensors

Scientific Reports
Multidisciplinary
2019English

Application of Machine Learning to Computer Graphics

IEEE Computer Graphics and Applications
Computer GraphicsComputer-Aided DesignSoftware
2018English

Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites

Fibers
Mechanics of MaterialsCivilStructural EngineeringBiomaterialsCompositesCeramics
2019English

The Application of Distributed Optical Strain Sensing to Measure the Strain Distribution of Ground Support Members

Facets
Multidisciplinary
2018English

Machine Learning to Predict Final Fire Size at the Time of Ignition

International Journal of Wildland Fire
ForestryEcology
2019English

Enhanced Thermoelectric Performance of CNT/P3HT Composites With Low CNT Content

RSC Advances
ChemistryChemical Engineering
2018English

Can Machine Learning Help Predict the Outcome of Asylum Adjudications?

2017English

A Machine Learning Approach to Predict Blastocyst Formation in Vitro

Fertility and Sterility
GynecologyReproductive MedicineObstetrics
2019English

Using Network Theory and Machine Learning to Predict El Niño

Earth System Dynamics
EarthPlanetary Sciences
2018English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2026 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy