Quantification of similarity and physical awareness of microstructures generated via generative models
出版年份 2023 全文链接
标题
Quantification of similarity and physical awareness of microstructures generated via generative models
作者
关键词
-
出版物
COMPUTATIONAL MATERIALS SCIENCE
Volume 221, Issue -, Pages 112074
出版商
Elsevier BV
发表日期
2023-02-15
DOI
10.1016/j.commatsci.2023.112074
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- (2022) Kanglin Liu et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Deep Generative Models in Engineering Design: A Review
- (2022) Lyle Regenwetter et al. JOURNAL OF MECHANICAL DESIGN
- Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder
- (2021) Yongju Kim et al. MATERIALS & DESIGN
- A Machine-Learning Approach to Predict Creep Properties of Cr-Mo Steel with Time-Temperature Parameters
- (2021) Jiaqi Wang et al. Journal of Materials Research and Technology-JMR&T
- Comparative Study of Deep Generative Models on Chemical Space Coverage
- (2021) Jie Zhang et al. Journal of Chemical Information and Modeling
- Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
- (2021) Jaimyun Jung et al. npj Computational Materials
- Pros and cons of GAN evaluation measures: New developments
- (2021) Ali Borji COMPUTER VISION AND IMAGE UNDERSTANDING
- Machine Learning for Materials Scientists: An introductory guide towards best practices
- (2020) Anthony Yu-Tung Wang et al. CHEMISTRY OF MATERIALS
- Accelerating the Discovery of New DP Steel Using Machine Learning-Based Multiscale Materials Simulations
- (2020) Abdallah A. Chehade et al. METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE
- Latent Space Manipulation for High-Resolution Medical Image Synthesis via the StyleGAN
- (2020) Lukas Fetty et al. Zeitschrift fur Medizinische Physik
- Generative Adversarial Networks for Crystal Structure Prediction
- (2020) Sungwon Kim et al. ACS Central Science
- Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
- (2020) Yabo Dan et al. npj Computational Materials
- Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships
- (2020) Steven B. Torrisi et al. npj Computational Materials
- Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks
- (2020) Aditya Venkatraman et al. JOM
- Deep generative modeling for mechanistic-based learning and design of metamaterial systems
- (2020) Liwei Wang et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach
- (2019) Yuanjie Zhi et al. Metals
- Microstructural damage sensitivity prediction using spatial statistics
- (2019) B. C. Cameron et al. Scientific Reports
- Comparisons of Different Data-Driven Modeling Techniques for Predicting Tensile Strength of X70 Pipeline Steels
- (2019) Siwei Wu et al. TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS
- An efficient regularized K-nearest neighbor structural twin support vector machine
- (2019) Fan Xie et al. APPLIED INTELLIGENCE
- Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning
- (2019) Carl Kusche et al. PLoS One
- Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model
- (2018) Ruijin Cang et al. COMPUTATIONAL MATERIALS SCIENCE
- An online tool for predicting fatigue strength of steel alloys based on ensemble data mining
- (2018) Ankit Agrawal et al. INTERNATIONAL JOURNAL OF FATIGUE
- Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques
- (2018) Ramin Bostanabad et al. PROGRESS IN MATERIALS SCIENCE
- Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks
- (2018) Lukas Mosser et al. TRANSPORT IN POROUS MEDIA
- A new framework for rotationally invariant two-point spatial correlations in microstructure datasets
- (2018) Ahmet Cecen et al. ACTA MATERIALIA
- Accelerating multi-point statistics reconstruction method for porous media via deep learning
- (2018) Junxi Feng et al. ACTA MATERIALIA
- Microstructural Materials Design via Deep Adversarial Learning Methodology
- (2018) Zijiang Yang et al. JOURNAL OF MECHANICAL DESIGN
- An efficient machine learning approach to establish structure-property linkages
- (2018) Jaimyun Jung et al. COMPUTATIONAL MATERIALS SCIENCE
- Bayesian approach in predicting mechanical properties of materials: Application to dual phase steels
- (2018) Jaimyun Jung et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Quantitative detection of harmful elements in alloy steel by LIBS technique and sequential backward selection-random forest (SBS-RF)
- (2017) Fangqi Ruan et al. JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
- Microstructural effects on the average properties in porous battery electrodes
- (2016) Ramiro García-García et al. JOURNAL OF POWER SOURCES
- Theory-Guided Machine Learning in Materials Science
- (2016) Nicholas Wagner et al. Frontiers in Materials
- An Overview of Dual-Phase Steels: Advances in Microstructure-Oriented Processing and Micromechanically Guided Design
- (2015) C.C. Tasan et al. Annual Review of Materials Research
- Machine learning of single molecule free energy surfaces and the impact of chemistry and environment upon structure and dynamics
- (2015) Rachael A. Mansbach et al. JOURNAL OF CHEMICAL PHYSICS
- Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials
- (2014) Surya R. Kalidindi INTERNATIONAL MATERIALS REVIEWS
- Stochastic generation of explicit pore structures by thresholding Gaussian random fields
- (2014) Jeffrey D. Hyman et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Quantitative Determination of Organic Semiconductor Microstructure from the Molecular to Device Scale
- (2012) Jonathan Rivnay et al. CHEMICAL REVIEWS
- Continuum Models of Ductile Fracture: A Review
- (2009) J. Besson INTERNATIONAL JOURNAL OF DAMAGE MECHANICS
- Microstructure sensitive design for performance optimization
- (2009) David T. Fullwood et al. PROGRESS IN MATERIALS SCIENCE
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