UB Paderborn / Katalog / Suche / in CDI

Sortiert nach

Einfacher Volltextzugriff von (fast) überall

Installieren Sie die LibKey Nomad-Browsererweiterung und erhalten Sie von vielen Webseiten einfachen Zugriff auf lizenzierte Volltexte oder Open Access Publikationen. Mehr erfahren...

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

APL materials, 2016-05, Vol.4 (5), p.053208-053208-10

2016

Volltextzugriff (PDF)

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Nature communications, 2019-11, Vol.10 (1), p.5316-5316, Article 5316

2019

Volltextzugriff (PDF)

Deep materials informatics: Applications of deep learning in materials science

MRS communications, 2019-09, Vol.9 (3), p.779-792

2019

Volltextzugriff (PDF)

Microstructural Materials Design Via Deep Adversarial Learning Methodology

Journal of mechanical design (1990), 2018-11, Vol.140 (11)

2018

Volltextzugriff (PDF)

A general-purpose machine learning framework for predicting properties of inorganic materials

npj computational materials, 2016-08, Vol.2 (1), p.16028, Article 16028

2016

Volltextzugriff (PDF)

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Nature communications, 2021-11, Vol.12 (1), p.6595-6595, Article 6595

2021

Volltextzugriff (PDF)

Recent advances and applications of deep learning methods in materials science

npj computational materials, 2022-04, Vol.8 (1), p.1-26, Article 59

2022

Volltextzugriff (PDF)

Value stream mapping to reduce the lead-time of a product development process

International journal of production economics, 2015-02, Vol.160, p.202-212

2015

Volltextzugriff (PDF)

Tactical supply chain planning under a carbon tax policy scheme: A case study

International journal of production economics, 2015-06, Vol.164, p.206-215

2015

Volltextzugriff (PDF)

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

Scientific reports, 2018-12, Vol.8 (1), p.17593-13, Article 17593

2018

Volltextzugriff (PDF)

A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures

Journal of mechanical design (1990), 2015-05, Vol.137 (5)

2015

Volltextzugriff (PDF)

A predictive machine learning approach for microstructure optimization and materials design

Scientific reports, 2015-06, Vol.5 (1), p.11551-11551, Article 11551

2015

Volltextzugriff (PDF)

Enabling deeper learning on big data for materials informatics applications

Scientific reports, 2021-02, Vol.11 (1), p.4244-4244, Article 4244

2021

Volltextzugriff (PDF)

Predictive analytics for crystalline materials: bulk modulus

RSC advances, 2016-01, Vol.6 (97), p.95246-95251

2016

Volltextzugriff (PDF)

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

Physical review. B, 2017-07, Vol.96 (2), Article 024104

2017

Volltextzugriff (PDF)

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Transportation research. Part F, Traffic psychology and behaviour, 2019-01, Vol.60, p.366-382

2019

Volltextzugriff (PDF)

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

Journal of computational chemistry, 2018-02, Vol.39 (4), p.191-202

2018

Volltextzugriff (PDF)

Robust IgM responses following intravenous vaccination with Bacille Calmette-Guérin associate with prevention of Mycobacterium tuberculosis infection in macaques

Nature immunology, 2021-12, Vol.22 (12), p.1515-1523

2021

Volltextzugriff (PDF)

Moving closer to experimental level materials property prediction using AI

Scientific reports, 2022-07, Vol.12 (1), p.11953-11953, Article 11953

2022

Volltextzugriff (PDF)

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

Computational materials science, 2018-08, Vol.151 (C), p.278-287

2018

Volltextzugriff (PDF)

Improving deep learning model performance under parametric constraints for materials informatics applications

Scientific reports, 2023-06, Vol.13 (1), p.9128-9128, Article 9128

2023

Volltextzugriff (PDF)

Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

Acta materialia, 2019-03, Vol.166 (C), p.335-345

2019

Volltextzugriff (PDF)

Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

Integrating materials and manufacturing innovation, 2014-12, Vol.3 (1), p.90-108

2014

Volltextzugriff (PDF)

Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Nature communications, 2020-07, Vol.11 (1), p.3643-3643, Article 3643

2020

Volltextzugriff (PDF)

Survivability prediction of colon cancer patients using neural networks

Health informatics journal, 2019-09, Vol.25 (3), p.878-891

2019

Volltextzugriff (PDF)

1 – 25 von 795

Suche als RSS-Feed

Suchergebnisse filtern

Filter anzeigen

Nur zeigen

Geprüft (Peer-Review)
(451)
Mit Volltextzugriff
(693)

Publikationsform

3 weitere anzeigen...

Weniger anzeigen...

Sprache

Erscheinungsjahr

n.n

Thema

Science & Technology
(435)
Technology
(336)
Computer Science
(268)
Computer Science, Theory & Methods
(148)
Engineering
(140)
Engineering, Electrical & Electronic
(88)
Life Sciences & Biomedicine
(87)
Materials Science
(67)
Computer Science, Hardware & Architecture
(66)
Software
(65)
Exact Sciences And Technology
(60)
Computer Science, Information Systems
(58)
Applied Sciences
(55)
Data Mining
(52)
Physical Sciences
(49)
Computer Science; Control Theory; Systems
(46)
Computer Science, Artificial Intelligence
(45)
Machine Learning
(43)
Materials Science, Multidisciplinary
(42)
Concurrent Computing
(38)

15 weitere anzeigen...

Weniger anzeigen...

Neu hinzugefügt

Menü

Weitere Dienste

Einstellungen

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Deep materials informatics: Applications of deep learning in materials science

Microstructural Materials Design Via Deep Adversarial Learning Methodology

A general-purpose machine learning framework for predicting properties of inorganic materials

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Recent advances and applications of deep learning methods in materials science

Value stream mapping to reduce the lead-time of a product development process

Tactical supply chain planning under a carbon tax policy scheme: A case study

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures

A predictive machine learning approach for microstructure optimization and materials design

Enabling deeper learning on big data for materials informatics applications

Predictive analytics for crystalline materials: bulk modulus

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

Robust IgM responses following intravenous vaccination with Bacille Calmette-Guérin associate with prevention of Mycobacterium tuberculosis infection in macaques

Moving closer to experimental level materials property prediction using AI

Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets

Improving deep learning model performance under parametric constraints for materials informatics applications

Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches

Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

Author Correction: Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Survivability prediction of colon cancer patients using neural networks

Suchergebnisse filtern