4.5 Article

Quantification of similarity and physical awareness of microstructures generated via generative models

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Pros and cons of GAN evaluation measures: New developments

Ali Borji

Summary: This work presents new technologies and important areas concerning GAN evaluation, highlights emerging dimensions in assessing models, and discusses the connection with deepfakes.

COMPUTER VISION AND IMAGE UNDERSTANDING (2022)

Article Computer Science, Artificial Intelligence

Towards Disentangling Latent Space for Unsupervised Semantic Face Editing

Kanglin Liu et al.

Summary: In this paper, a new technique called STIA-WO is presented to disentangle the latent space for unsupervised semantic face editing. By applying STIA-WO to GAN, a StyleGAN named STGAN-WO is developed, which achieves better attribute editing than state of the art methods.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2022)

Review Engineering, Mechanical

Deep Generative Models in Engineering Design: A Review

Lyle Regenwetter et al.

Summary: Automated design synthesis has the potential to revolutionize the engineering design process, and deep generative machine learning models have shown promising results in applications like structural optimization, materials design, and shape synthesis. However, there are still challenges and limitations in using DGMs in design fields, such as design creativity, constraint handling, and modeling both form and functional performance simultaneously. Future work should focus on addressing these challenges.

JOURNAL OF MECHANICAL DESIGN (2022)

Article Engineering, Manufacturing

Reduced-Order Damage Assessment Model for Dual-Phase Steels

Sanket Thakre et al.

Summary: The proposed reduced-order model uses random forest regression to predict damage initiation in dual-phase steels, evaluating the ductile damage behavior of different microstructures. A general framework is introduced to rank the severity of damage initiation using statistical fitting, successfully grouping the classes into three major clusters. This framework is a step towards developing more effective and invertible reduced-order structure-property correlations.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2022)

Article Materials Science, Multidisciplinary

Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder

Yongju Kim et al.

Summary: Data-driven approaches with deep learning-based image processing, utilizing variational autoencoders to generate continuous microstructure spaces, help explore structure-property relationships for designing new advanced materials. By using finite element method simulations and Gaussian process regression with VAE, accurate predictions of microstructures with target mechanical properties can be achieved in a continuous manner.

MATERIALS & DESIGN (2021)

Article Materials Science, Multidisciplinary

A machine-learning approach to predict creep properties of Cr-Mo steel with time-temperature parameters

Jiaqi Wang et al.

Summary: The study explores the idea of using machine learning to predict the creep life of Cr-Mo steel by transforming creep life into time-temperature parameters, which improved prediction accuracy. Random forest had the best accuracy and robustness when predicting creep life using Manson-Succop parameter as the target feature, providing new insights for analyzing the main influencing features to creep property of Cr-Mo steel.

JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T (2021)

Article Chemistry, Medicinal

Comparative Study of Deep Generative Models on Chemical Space Coverage

Jie Zhang et al.

Summary: This study introduces a novel metric based on chemical space coverage for evaluating and comparing the performance of deep molecular generative models. Experimental results show significant performance variations among different generative models when using limited training data, allowing for clear differentiation of models with stronger generalization capabilities.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

Article Chemistry, Physical

Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis

Jaimyun Jung et al.

Summary: Digital microstructures are crucial in modern materials research, but their resolution-sensitive features may be overlooked due to inadequate resolution, limiting the accuracy of microstructure characterization and material analysis. Super-resolution imaging based on deep learning can enhance the quality of low-resolution microstructure data, allowing for more accurate microstructure characterization and finite element mechanical analysis.

NPJ COMPUTATIONAL MATERIALS (2021)

Article Computer Science, Theory & Methods

A Survey on Generative Adversarial Networks: Variants, Applications, and Training

Abdul Jabbar et al.

Summary: Generative models, particularly Generative Adversarial Networks (GAN), have received significant attention for their remarkable data generation capability. However, stable training remains a crucial issue. This study surveys various training solutions, including the original GAN model and modifications, analysis of applications in different domains, and detailed research on training obstacles and solutions.

ACM COMPUTING SURVEYS (2021)

Article Engineering, Manufacturing

Estimation of Local Strain Fields in Two-Phase Elastic Composite Materials Using UNet-Based Deep Learning

Mayank Raj et al.

Summary: This study utilizes the UNet algorithm to predict local strain fields in two-phase composite materials under uniaxial tensile load. The model achieves a 94% R-2 score on a test dataset of 1200 different microstructures. Detailed statistical analysis is conducted to understand the impact of volume fraction and elastic modulus ratio on the deep learning model's trainability.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2021)

Article Engineering, Manufacturing

Intrinsic Dimensionality of Microstructure Data

Sanket Thakre et al.

Summary: This paper presents a simple and unique approach to estimate the intrinsic dimensionality of microstructure data, investigating the effects of global and local metrics on various classes of 2D and 3D synthetic two-phase microstructure data through PCA and MDS analysis. The study found that 2-point spatial correlation statistics, phase fraction, and the inherent stochastic nature of the microstructure have significant influences on intrinsic dimensionality.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2021)

Article Chemistry, Physical

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

Anthony Yu-Tung Wang et al.

CHEMISTRY OF MATERIALS (2020)

Article Materials Science, Multidisciplinary

Accelerating the Discovery of New DP Steel Using Machine Learning-Based Multiscale Materials Simulations

Abdallah A. Chehade et al.

METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE (2020)

Article Chemistry, Multidisciplinary

Generative Adversarial Networks for Crystal Structure Prediction

Sungwon Kim et al.

ACS CENTRAL SCIENCE (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Latent space manipulation for high-resolution medical image synthesis via the StyleGAN

Lukas Fetty et al.

ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK (2020)

Article Engineering, Multidisciplinary

Deep generative modeling for mechanistic-based learning and design of metamaterial systems

Liwei Wang et al.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)

Article Materials Science, Multidisciplinary

The differences of damage initiation and accumulation of DP steels: a numerical and experimental analysis

Felix Puetz et al.

INTERNATIONAL JOURNAL OF FRACTURE (2020)

Article Engineering, Manufacturing

Model Selection and Evaluation for Machine Learning: Deep Learning in Materials Processing

Adam Kopper et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2020)

Article Metallurgy & Metallurgical Engineering

Comparisons of Different Data-Driven Modeling Techniques for Predicting Tensile Strength of X70 Pipeline Steels

Siwei Wu et al.

TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS (2019)

Article Multidisciplinary Sciences

Microstructural damage sensitivity prediction using spatial statistics

B. C. Cameron et al.

SCIENTIFIC REPORTS (2019)

Article Computer Science, Artificial Intelligence

An efficient regularized K-nearest neighbor structural twin support vector machine

Fan Xie et al.

APPLIED INTELLIGENCE (2019)

Article Materials Science, Multidisciplinary

An efficient machine learning approach to establish structure-property linkages

Jaimyun Jung et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

Article Nanoscience & Nanotechnology

Bayesian approach in predicting mechanical properties of materials: Application to dual phase steels

Jaimyun Jung et al.

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2019)

Article Engineering, Mechanical

An online tool for predicting fatigue strength of steel alloys based on ensemble data mining

Ankit Agrawal et al.

INTERNATIONAL JOURNAL OF FATIGUE (2018)

Review Materials Science, Multidisciplinary

Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques

Ramin Bostanabad et al.

PROGRESS IN MATERIALS SCIENCE (2018)

Article Engineering, Chemical

Stochastic Reconstruction of an Oolitic Limestone by Generative Adversarial Networks

Lukas Mosser et al.

TRANSPORT IN POROUS MEDIA (2018)

Article Materials Science, Multidisciplinary

A new framework for rotationally invariant two-point spatial correlations in microstructure datasets

Ahmet Cecen et al.

ACTA MATERIALIA (2018)

Article Materials Science, Multidisciplinary

Accelerating multi-point statistics reconstruction method for porous media via deep learning

Junxi Feng et al.

ACTA MATERIALIA (2018)

Article Engineering, Mechanical

Microstructural Materials Design Via Deep Adversarial Learning Methodology

Zijiang Yang et al.

JOURNAL OF MECHANICAL DESIGN (2018)

Article Engineering, Manufacturing

Reduced-Order Microstructure-Sensitive Models for Damage Initiation in Two-Phase Composites

David Montes de Oca Zapiain et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2018)

Article Engineering, Manufacturing

Machine Learning-Based Reduce Order Crystal Plasticity Modeling for ICME Applications

Mengfei Yuan et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2018)

Article Physics, Fluids & Plasmas

Reconstruction of three-dimensional porous media using generative adversarial neural networks

Lukas Mosser et al.

PHYSICAL REVIEW E (2017)

Article Chemistry, Physical

Microstructural effects on the average properties in porous battery electrodes

Ramiro Garcia-Garcia et al.

JOURNAL OF POWER SOURCES (2016)

Article Materials Science, Multidisciplinary

Theory-guided Machine learning in Materials science

Nicholas Wagner et al.

FRONTIERS IN MATERIALS (2016)

Article Engineering, Manufacturing

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Ahmet Cecen et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2016)

Review Materials Science, Multidisciplinary

An Overview of Dual-Phase Steels: Advances in Microstructure-Oriented Processing and Micromechanically Guided Design

C. C. Tasan et al.

ANNUAL REVIEW OF MATERIALS RESEARCH, VOL 45 (2015)

Review Materials Science, Multidisciplinary

Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials

Surya R. Kalidindi

INTERNATIONAL MATERIALS REVIEWS (2015)

Article Computer Science, Interdisciplinary Applications

Stochastic generation of explicit pore structures by thresholding Gaussian random fields

Jeffrey D. Hyman et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2014)

Article Engineering, Manufacturing

Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data

Stephen R. Niezgoda et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2013)

Review Chemistry, Multidisciplinary

Quantitative Determination of Organic Semiconductor Microstructure from the Molecular to Device Scale

Jonathan Rivnay et al.

CHEMICAL REVIEWS (2012)

Review Materials Science, Multidisciplinary

Continuum Models of Ductile Fracture: A Review

J. Besson

INTERNATIONAL JOURNAL OF DAMAGE MECHANICS (2010)

Review Materials Science, Multidisciplinary

Microstructure sensitive design for performance optimization

David T. Fullwood et al.

PROGRESS IN MATERIALS SCIENCE (2010)

Article Computer Science, Artificial Intelligence

Image quality assessment: From error visibility to structural similarity

Z Wang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2004)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)