@phdthesis{Ostrowski2018,
  author    = {Ostrowski, Max},
  title     = {Modern constraint answer set solving},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407799},
  school      = {Universit{\"a}t Potsdam},
  pages     = {135},
  year      = {2018},
  abstract  = {Answer Set Programming (ASP) is a declarative problem solving approach, combining a rich yet simple modeling language with high-performance solving capabilities. Although this has already resulted in various applications, certain aspects of such applications are more naturally modeled using variables over finite domains, for accounting for resources, fine timings, coordinates, or functions. Our goal is thus to extend ASP with constraints over integers while preserving its declarative nature. This allows for fast prototyping and elaboration tolerant problem descriptions of resource related applications. The resulting paradigm is called Constraint Answer Set Programming (CASP). We present three different approaches for solving CASP problems. The first one, a lazy, modular approach combines an ASP solver with an external system for handling constraints. This approach has the advantage that two state of the art technologies work hand in hand to solve the problem, each concentrating on its part of the problem. The drawback is that inter-constraint dependencies cannot be communicated back to the ASP solver, impeding its learning algorithm. The second approach translates all constraints to ASP. Using the appropriate encoding techniques, this results in a very fast, monolithic system. Unfortunately, due to the large, explicit representation of constraints and variables, translation techniques are restricted to small and mid-sized domains. The third approach merges the lazy and the translational approach, combining the strength of both while removing their weaknesses. To this end, we enhance the dedicated learning techniques of an ASP solver with the inferences of the translating approach in a lazy way. That is, the important knowledge is only made explicit when needed. By using state of the art techniques from neighboring fields, we provide ways to tackle real world, industrial size problems. By extending CASP to reactive solving, we open up new application areas such as online planning with continuous domains and durations.},
  language  = {en}
}
@phdthesis{Kar,
  author    = {Kar, Manaswita},
  title     = {Energy band gap tuning of halide perovskite materials from first principles},
  school      = {Universit{\"a}t Potsdam},
  abstract  = {Solar cells based on hybrid perovskites materials have become significantly important among the third generation photovoltaics over the last few years. The first solid state solar cell was reported in 2012. Over the years, the power conversion efficiencies of these devices have increased at a tremendous pace and this has made the perovskite solar cell devices a serious competitor in the well-established market of thin-film and wafer technologies. Over time, a large number of articles on this topic has been published in peer-reviewed journals. The presence of lead in the most efficient hybrid perovskite materials have raised questions about the possible toxicity of these devices and the extent of their environmental impact. Therefore, a lot of research has been devoted to finding alternative perovskite materials with similar or even better opto-electronic properties. An alternative strategy to improve the efficiency of thin film solar cells is to build efficient tandem cells by combining two or more perovskite materials with specifically tailored band gaps. The first step towards the development of perovskite-only tandem solar cells is to identify complementary hybrid perovskite materials with specific band gaps that maximize the efficiency of tandem solar cells. The optimal set of optical gaps for a tandem structure made of two materials is 1.9 eV and 1.0 eV. Since the electronic properties of hybrid perovskites are known to be strongly dependent on the composition and distortion of the crystal lattice, strong focus has been made towards the structure optimisation as well as the calculation of the energy band gaps of the materials using density functional theory (DFT). In an attempt to study the structure-property relationship of these perovskite materials and to find novel perovskite materials for future applications, researchers have employed computational screening procedures to study a large range of these materials by systematic replacement of the cations and anions from the prototypical perovskite. Density functional theory in particular is used as a theoretical tool, because of it's precision to determine the properties of materials and also it's computational viability in dealing with complex systems. In this thesis, the main focus is to do a systematic screening of the perovskite materials, of the composition ABX3 again by replacing the A-site, B-site and the X-site elements to find novel materials with band gaps suitable for application in tandem solar cells. As a first step towards contributing to this vibrant field of research, a high-throughput computational screening has been performed by replacing the metal and the halogen in the conventional CH3NH3PbI3 perovskites with homovalent metals and halogens to find materials in the desired range of band gaps that has already been mentioned earlier. This is achieved by performing a geometry optimisation on all the simulated structures followed by calculating their energy band gaps at the semilocal and the hybrid levels of theory. However, it is well known that the rotation of the organic cation CH3NH3 hinders the stability of these devices by the formation of hydrogen bonds between the hydrogen atoms of the cation and the halogens. This causes the materials to degrade under normal temperature and pressure conditions. As an attempt to prevent these devices from being unstable, a next step has been taken where the CH3NH3 cation has been replaced by inorganic cations of similar ionic radius. This is followed by another thorough screening, similar to the previous step. The stability of the materials has been determined by using the empirical Goldschmidt tolerance factor. As a last part of the thesis, a small proportion of the inorganic cation is mixed with CH3NH3 in order to form mixed-halide perovskites. These structures are optimised and their band gaps are calculated using density functional theory in order to predict materials suitable for single junction as well as tandem solar cell devices. It is expected that the contribution made through this thesis will be helpful for the progress of perovskite solar cells in terms of efficiencies and will also allow the community to explore the different properties these materials for further progress and development.},
  language  = {en}
}
@phdthesis{Papenbrock2017,
  author    = {Papenbrock, Thorsten},
  title     = {Data profiling - efficient discovery of dependencies},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-406705},
  school      = {Universit{\"a}t Potsdam},
  pages     = {viii, ii, 141},
  year      = {2017},
  abstract  = {Data profiling is the computer science discipline of analyzing a given dataset for its metadata. The types of metadata range from basic statistics, such as tuple counts, column aggregations, and value distributions, to much more complex structures, in particular inclusion dependencies (INDs), unique column combinations (UCCs), and functional dependencies (FDs). If present, these statistics and structures serve to efficiently store, query, change, and understand the data. Most datasets, however, do not provide their metadata explicitly so that data scientists need to profile them. While basic statistics are relatively easy to calculate, more complex structures present difficult, mostly NP-complete discovery tasks; even with good domain knowledge, it is hardly possible to detect them manually. Therefore, various profiling algorithms have been developed to automate the discovery. None of them, however, can process datasets of typical real-world size, because their resource consumptions and/or execution times exceed effective limits. In this thesis, we propose novel profiling algorithms that automatically discover the three most popular types of complex metadata, namely INDs, UCCs, and FDs, which all describe different kinds of key dependencies. The task is to extract all valid occurrences from a given relational instance. The three algorithms build upon known techniques from related work and complement them with algorithmic paradigms, such as divide \& conquer, hybrid search, progressivity, memory sensitivity, parallelization, and additional pruning to greatly improve upon current limitations. Our experiments show that the proposed algorithms are orders of magnitude faster than related work. They are, in particular, now able to process datasets of real-world, i.e., multiple gigabytes size with reasonable memory and time consumption. Due to the importance of data profiling in practice, industry has built various profiling tools to support data scientists in their quest for metadata. These tools provide good support for basic statistics and they are also able to validate individual dependencies, but they lack real discovery features even though some fundamental discovery techniques are known for more than 15 years. To close this gap, we developed Metanome, an extensible profiling platform that incorporates not only our own algorithms but also many further algorithms from other researchers. With Metanome, we make our research accessible to all data scientists and IT-professionals that are tasked with data profiling. Besides the actual metadata discovery, the platform also offers support for the ranking and visualization of metadata result sets. Being able to discover the entire set of syntactically valid metadata naturally introduces the subsequent task of extracting only the semantically meaningful parts. This is challenge, because the complete metadata results are surprisingly large (sometimes larger than the datasets itself) and judging their use case dependent semantic relevance is difficult. To show that the completeness of these metadata sets is extremely valuable for their usage, we finally exemplify the efficient processing and effective assessment of functional dependencies for the use case of schema normalization.},
  language  = {en}
}
@article{KuehnHainzlDahmetal.2022,
  author    = {K{\"u}hn, Daniela and Hainzl, Sebastian and Dahm, Torsten and Richter, Gudrun and Vera Rodriguez, Ismael},
  title     = {A review of source models to further the understanding of the seismicity of the Groningen field},
  series = {Netherlands journal of geosciences : NJG},
  volume    = {101},
  journal   = {Netherlands journal of geosciences : NJG},
  publisher = {Cambridge Univ. Press},
  address   = {Cambridge},
  issn      = {0016-7746},
  doi       = {10.1017/njg.2022.7},
  pages     = {12},
  year      = {2022},
  abstract  = {The occurrence of felt earthquakes due to gas production in Groningen has initiated numerous studies and model attempts to understand and quantify induced seismicity in this region. The whole bandwidth of available models spans the range from fully deterministic models to purely empirical and stochastic models. In this article, we summarise the most important model approaches, describing their main achievements and limitations. In addition, we discuss remaining open questions and potential future directions of development.},
  language  = {en}
}