Inverse design of spinodoid structures using Bayesian optimization

Tailoring materials to achieve a desired behavior in specific applications is of significant scientific and industrial interest as design of materials is a key driver to innovation. Overcoming the rather slow and expertise-bound traditional forward approaches of trial and error, inverse design is attracting substantial attention. Targeting a property, the design model proposes a candidate structure with the desired property. This concept can be particularly well applied to the field of architected materials as their structures can be directly tuned. The bone-like spinodoid materials are a specific class of architected materials. They are of considerable interest thanks to their non-periodicity, smoothness, and low-dimensional statistical description. Previous work successfully employed machine learning (ML) models for inverse design. The amount of data necessary for most ML approaches poses a severe obstacle for broader application, especially in the context of inelasticity. That is why we propose an inverse-design approach based on Bayesian optimization to operate in the small-data regime. Necessitating substantially less data, a small initial data set is iteratively augmented by in silico generated data until a structure with the targeted properties is found. The application to the inverse design of spinodoid structures of desired elastic properties demonstrates the framework's potential for paving the way for advance in inverse design

Scattering transform in microstructure reconstruction

Descriptor-based microstructure characterization plays a crucial role in the field of reversed material engineering for random heterogeneous media. With the advent of differentiable microstructure characterization and reconstruction, there has been a growing interest in the development of differentiable formulations of descriptors. The search for effective descriptors becomes indispensable to adequately characterize a wide range of microstructures. This work proposes a novel approach to construct a descriptor by utilizing a wavelet-based transformation called the scattering transformation on microstructure images. The characterization and reconstruction capabilities of this newly developed descriptor are compared to a benchmark descriptor based on spatial correlation functions using various 2D microstructure images. The comparative analysis aims to evaluate the effectiveness and potential advantages of the proposed wavelet-based descriptor

Conditional diffusion-based microstructure reconstruction

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 highly diverse and morphologically complex data set is created by combining and processing databases from the literature, where the reconstruction of realistic micrographs for a given material class demonstrates the ability of the model to capture these features. Furthermore, a fiber composite data set is used to validate the applicability of diffusion models to small data set sizes that can realistically be created by a single lab. The quality and diversity of the reconstructed microstructures is quantified by means of descriptor-based error metrics as well as the Fr\'echet inception distance (FID) score. Although not present in the training data set, the generated samples are visually indistinguishable from real data to the untrained eye and various error metrics are computed. This demonstrates the utility of diffusion models in microstructure reconstruction and provides a basis for further extensions such as 2D-to-3D reconstruction or application to multiscale modeling and structure-property linkages

The impact of favouritism on the business climate: a study on Wasta in Jordan

"Wide-ranging agreement exists today that a good business climate is central to economic growth and poverty alleviation. But questions remain open about the role of the state in creating a good business climate. This study is intended to answer some of these questions. The Arab Human Development Report 2004 stresses that sustainable economic growth cannot be achieved in the MENA countries without improved governance. One of the core dimensions of good governance is transparency and the control of corruption. The latter in particular is important for a good business climate (World Bank 2005f). Corruption can take different forms, one of them being favouritism which is very widespread in the MENA region. It is usually referred to there as 'wasta', which is Arabic for 'relation' or 'connection', and describes the use of personal relations for preferential treatment. The present study focuses on the economic effects of wasta. It has a twofold aim: first to find out how the use of wasta in state-business relations affects the business climate and investment and, thereby, economic development at large, and, second, to identify starting points for curtailing the use of wasta. All reference is hereby to Jordan, which has been selected for this case study for pragmatic reasons. Chapter 2 presents the conceptual framework of the study: It defines first the key terms and concepts of the study: business climate, state-business relations, favouritism, and wasta. After that, it discusses in general terms why and how favouritism may affect the business climate. Finally, the chapter draws upon theoretical literature to give possible explanations for the prevalence of favouritism in a given country. Chapter 3 is based on the results of Chapter 2 and delineates the hypotheses tested by our empirical research in Jordan. In addition, it gives a detailed account of our research methodology. Chapter 4 outlines the main features of the business climate in Jordan: It identifies the main problems perceived by businesspeople and discusses possible reasons for these weaknesses. The chapter concludes that statebusiness relations are a major area of concern for investors in Jordan. Chapter 5 turns to the role of wasta in Jordan. It shows how the use of wasta permeates all areas of economic and social life and gives an insight into the ambiguous attitudes of Jordanians toward wasta. Chapter 6 is devoted to the effects of wasta on the business climate and on investment. It shows that the prevalence of wasta in Jordan makes statebusiness relations unfair and inefficient. In addition, wasta establishes incentives for investment in social relations rather than in productive capital,thus lowering the rate of capital formation in Jordan. Chapter 7 concludes with policy recommendations." (excerpt

Reconstructing microstructures from statistical descriptors using neural cellular automata

The problem of generating microstructures of complex materials in silico has been approached from various directions including simulation, Markov, deep learning and descriptor-based approaches. This work presents a hybrid method that is inspired by all four categories and has interesting scalability properties. A neural cellular automaton is trained to evolve microstructures based on local information. Unlike most machine learning-based approaches, it does not directly require a data set of reference micrographs, but is trained from statistical microstructure descriptors that can stem from a single reference. This means that the training cost scales only with the complexity of the structure and associated descriptors. Since the size of the reconstructed structures can be set during inference, even extremely large structures can be efficiently generated. Similarly, the method is very efficient if many structures are to be reconstructed from the same descriptor for statistical evaluations. The method is formulated and discussed in detail by means of various numerical experiments, demonstrating its utility and scalability

Two-stage 2D-to-3D reconstruction of realistic microstructures: Implementation and numerical validation by effective properties

Realistic microscale domains are an essential step towards making modern multiscale simulations more applicable to computational materials engineering. For this purpose, 3D computed tomography scans can be very expensive or technically impossible for certain materials, whereas 2D information can be easier obtained. Based on a single or three orthogonal 2D slices, the recently proposed differentiable microstructure characterization and reconstruction (DMCR) algorithm is able to reconstruct multiple plausible 3D realizations of the microstructure based on statistical descriptors, i.e., without the need for a training data set. Building upon DMCR, this work introduces a highly accurate two-stage reconstruction algorithm that refines the DMCR results under consideration of microstructure descriptors. Furthermore, the 2D-to-3D reconstruction is validated using a real computed tomography (CT) scan of a recently developed beta-Ti/TiFe alloy as well as anisotropic "bone-like" spinodoid structures. After a detailed discussion of systematic errors in the descriptor space, the reconstructed microstructures are compared to the reference in terms of the numerically obtained effective elastic and plastic properties. Together with the free accessibility of the presented algorithms in MCRpy, the excellent results in this study motivate interdisciplinary cooperation in applying numerical multiscale simulations for computational materials engineering

Auswirkungen und Steuerung der Flächeninanspruchnahme im Stadt-Land-Nexus der Metropolregion Nürnberg

In den Jahren 2004 bis 2018 erfolgte in der Metropolregion Nürnberg eine Umnutzung von 70 410 Hektar landwirtschaftlicher Nutzfläche. Näherungsweise ergibt sich ein täglicher Flächenentzug von 13,7 ha. Rund 33 % entfallen dabei auf Wald- und Gehölzflächen, 25 % auf Siedlungs- und Verkehrsflächen, 24 % auf naturnahe Flächen und 18 % auf sonstige Flächen. Die jeweiligen Ursachen der landwirtschaftlichen Flächenverluste sind jedoch vielgestaltig und erfordern eine kleinräumige Untersuchung, um die lokalen Treiber identifizieren zu können. Um zukünftig den landwirtschaftlichen Flächenentzug zu reduzieren, sollen gemeinsame Leitlinien auf kommunaler und regionaler Ebene helfen, ein nachhaltiges Landmanagement zu entwickeln

Reconstructing microstructures from statistical descriptors using neural cellular automata

The problem of generating microstructures of complex materials in silico has been approached from various directions including simulation, Markov, deep learning and descriptor-based approaches. This work presents a hybrid method that is inspired by all four categories and has interesting scalability properties. A neural cellular automaton is trained to evolve microstructures based on local information. Unlike most machine learning-based approaches, it does not directly require a data set of reference micrographs, but is trained from statistical microstructure descriptors that can stem from a single reference. This means that the training cost scales only with the complexity of the structure and associated descriptors. Since the size of the reconstructed structures can be set during inference, even extremely large structures can be efficiently generated. Similarly, the method is very efficient if many structures are to be reconstructed from the same descriptor for statistical evaluations. The method is formulated and discussed in detail by means of various numerical experiments, demonstrating its utility and scalability