Statistical Reconstruction of Microstructures Using Entropic Descriptors
R. Piasecki, W. Olchawa, D. Frączek, R. Wiśniowski
备注：
34 pages, 17 figures, rewritten and clarified with modified references.arXiv admin note: text overlap with arXiv:1508.03857
发表信息：
Transp Porous Med 125 (2018) 105-125; [Special Issue on Reconstruction of Porous Media and Materials and Its Applications, Issue Editors: Pejman Tahmasebi, Muhammad Sahimi]
We report a multiscale approach of broad applicability to stochastic reconstruction of multiphase materials, including porous ones. The approach devised uses an optimization method, such as the simulated annealing (SA) and the so-called entropic descriptors (EDs). For a binary pattern, they quantify spatial inhomogeneity or statistical complexity at discrete length-scales. The EDs extract dissimilar structural information to that given by two-point correlation functions (CFs). Within the SA, we use an appropriate cost function consisting of EDs or comprised of EDs and CFs. It was found that the stochastic reconstruction is computationally efficient when we begin with a preliminary synthetic configuration having in part desirable features.
Another option is low-cost approximate reconstructing of the entire multiphase medium beyond the SA technique. The information included in the target ED-curve was utilized for this purpose. For a given volume fraction the low-cost trial microstructures can be generated in two ways. In the first one, applied to ceramics and carbonate samples, the interpenetrating spheres generate a number of trial configurations. In the second one, with phase-EDs, here used to the sandstone sample, the overlapping superspheres do it. Both methods use a radius determined from the EDs-linked two-exponent power-law. However, the supersphere deformation parameter allows controlling of the spatial inhomogeneity of prototypical microstructures. At last, even for a hypothetical ED-curve (under reasonable assumptions), the specific microstructure can be found, if it is realizable for a given volume fraction. In general, the EDs-based methods offer a compromise between computational efficiency and the accuracy of reconstructions.
Microstructure reconstruction is an important cornerstone to the inverse materials design concept. In this work, a general algorithm is developed to reconstruct a three-dimensional microstructure from given descriptors. Based on two-dimensional (2D) micrographs, this reconstruction algorithm allows valuable insight through spatial visualization of the microstructure and in silico studies of structure-property linkages. The formulation ensures computational efficiency by casting microstructure reconstruction as a gradient-based optimization problem. Herein, the descriptors can be chosen freely,…
Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress has been made in MCR recently, however, in absence of a flexible software platform it is difficult to use ideas from other researchers and to develop them further. To address this issue, this work presents MCRpy for easy-to-use, extensible and flexible MCR. The software platform that can be used as a program with graphical user interface, as a command line tool and as a Python library. The central idea is that micros…
Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
The Ohio State University（俄亥俄州立大学）；US Naval Research Laboratory（美国海军研究实验室）；Air Force Research Laboratory（空军研究实验室）；Blue Quartz Software, LLC（蓝石英软件有限责任公司）
Microstructure reconstruction has been an essential part of computational material engineering to reveal the relationship between microstructures and material properties. However, finding a general solution for microstructure characterization and reconstruction (MCR) tasks is still challenging, although there have been many attempts such as the descriptor-based MCR methods. To address this generality problem, the denoising diffusion models are first employed for the microstructure reconstruction task in this study. The applicability of the diffusion-based models is validated with several types…
The ability to image materials at the microscale from long-wavelength wave data is a major challenge to the geophysical, engineering and medical fields. Here, we present a framework to constrain microstructure geometry and properties from long-scale waves. To realistically quantify microstructures we use two-point statistics, from which we derive scale-dependent effective wave properties - wavespeed and attenuation - using strong-contrast expansions (SCE) for (visco)elastic wavefields. By evaluating various two-point correlation functions we observe that both effective wavespeeds and attenuati…
Microstructure reconstruction, a major component of inverse computational materials engineering, is currently advancing at an unprecedented rate. While various training-based and training-free approaches are developed, the majority of contributions are based on generative adversarial networks. In contrast, diffusion models constitute a more stable alternative, which have recently become the new state of the art and currently attract much attention. The present work investigates the applicability of diffusion models to the reconstruction of real-world microstructure data. For this purpose, a hi…
Stochastic microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s). This process enables computational analyses on ensembles of microstructures without having to perform exhaustive and costly experimental characterizations. Statistical functions-based and deep learning-based methods are among the stochastic microstructure reconstruction approaches applicable to a wide range of material systems. In this paper, we integrate statistical descriptors as well as feature maps from a pre-trained d…
Microstructure reconstruction is a key enabler of process-structure-property linkages, a central topic in materials engineering. Revisiting classical optimization-based reconstruction techniques,they are recognized as a powerful framework to reconstruct random heterogeneous media, especially due to their generality and controllability. The stochasticity of the available approaches is, however, identified as a performance bottleneck. In this work, reconstruction is approached as a differentiable optimization problem, where the error of a generic prescribed descriptor is minimized under consider…
Speeding up of microstructure reconstruction: I. Application to labyrinth patterns
最近，已经提出了基于二进制/灰度图案的微结构的蒙特卡罗混合重建的熵描述符（Piasecki 2011 Proc. R. A 467 806）。我们试图加快这种方法在这种情况下应用到一个二进制迷宫目标的重建。而不是一个随机的配置，我们建议从一个合适的合成模式创建的元胞自动机。目标特征属性的出现是降低计算成本的关键因素，计算成本可以用所需的MC步骤总数来衡量。对于相同的一组基本参数，我们研究了以下模拟场景：有偏/随机交替混合#2m方法，严格有偏#2b和随机/部分有偏#2rp方法。对每种情况进行25次运行。为了保持重建的可比精度，在最后阶段，我们使用的唯一选择程序是有偏的。这使我们能够对前三种情况进行一致的比较。仅为了方法的完整性而包括低效率的纯随机#2r方法。最后，在所建立的条件下，采用最快的混合#2m方法，给出了所有场景中最佳的单次重建和最佳的平均容差值。略慢的替代#2b和#2rp变体提供了相当但不太令人满意的结果。
Inferring Magnetic Material Parameters from Statistical Measures in Strongly Fluctuating Magnetization Dynamics
University of Duisburg-Essen（杜伊斯堡-埃森大学）；Center for Nanointegration Duisburg-Essen (CENIDE)（杜伊斯堡-埃森纳米集成中心）；Institute of Mechanics, University of Duisburg-Essen（杜伊斯堡-埃森大学力学研究所）；Department of Economics, University of Bamberg（班贝格大学经济系）；Functional Materials, Institute of Material Science, Technical University of Darmstadt（达姆施塔特工业大学材料科学学院功能材料部门）；Faculty of Chemistry, University of Duisburg-Essen（杜伊斯堡-埃森大学化学学院）
