.png){kind=icon}
返回
Statistically conditioned polycrystal generation using denoising diffusion models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.actamat.2024.119746.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Synthetic microstructure generation algorithms have emerged as a key tool for enabling large ICME and Materials Informatics efforts. In particular, statistically conditioned generative models allow researchers to systematically explore complex design spaces encountered in microstructure design. In spite of the engineering importance of polycrystalline materials, generative frameworks for these systems remain extremely limited. This stunted development - in comparison to the N -phase microstructure generation problem - occurs because of the complexities inherent to the representation of the polycrystalline orientation fields. For example, these fields exhibit multiple crystal- and sample -level symmetries. In prior work, these difficulties have resulted in instabilities in deep generative models for polycrystalline microstructures. In this work, we propose the use of a Reduced -Order Generalized Spherical Harmonic (ROGSH) basis to address the challenge described above. The proposed approach accounts for the complex sample- and crystal -level symmetries, and produces well behaved and low dimensional representations whose space has a meaningful Euclidean measure. We then demonstrate the ROGSH basis's remarkable ability to produce stable denoising diffusion models by using our recently established Local-Global generative framework to create visually realistic synthetic polycrystalline microstructures. Furthermore, we demonstrate that the generation process can be conditioned on both firstand second -order spatial statistics of the polycrystalline orientation fields.
Keyword:
Polycrystalline microstructure generation
Conditional microstructure generation
2-Point statistics
Denoising diffusion models
Spherical harmonics
期刊
IF:
9.3
论文数:
2.0W
被引数:
12.9W
机构
引用论文
Efficient generation of anisotropic N-field microstructures from 2-point statistics using multi-output Gaussian random fields使用多输出高斯随机场从2点统计信息有效生成各向异性N场微结构.png)
ACTA MATERIALIA
OAAI
ACTA MATERIALIAIF9.3
OAAI分享收藏Influence of macrozones on the fatigue cracking behavior and fracture mechanisms of rolled Ti-6Al-4V alloyMATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSINGIF7
PREAI
分享收藏Ultra-efficient reconstruction of 3D microstructure and distribution of properties of random heterogeneous materials containing multiple phasesACTA MATERIALIA
ACTA MATERIALIAIF9.3
PREAI
分享收藏Aquaporin-1 plays a key role in erythropoietin-induced endothelial cell migrationAquaporin-1在促红细胞生成素诱导的内皮细胞迁移中起关键作用OAAI
OAAI分享收藏Batch active learning for accelerating the development of interatomic potentials批量主动学习以加速原子间势的发展OAAI
OAAI分享收藏Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications晶体塑性有限元建模中的本构定律，运动学，均质化和多尺度方法概述: 理论，实验，应用ACTA MATERIALIA
ACTA MATERIALIAIF9.3
PREAI
分享收藏
查看更多内容