@unpublished{19524,
  abstract     = {{Object ranking is an important problem in the realm of preference learning.
On the basis of training data in the form of a set of rankings of objects,
which are typically represented as feature vectors, the goal is to learn a
ranking function that predicts a linear order of any new set of objects.
Current approaches commonly focus on ranking by scoring, i.e., on learning an
underlying latent utility function that seeks to capture the inherent utility
of each object. These approaches, however, are not able to take possible
effects of context-dependence into account, where context-dependence means that
the utility or usefulness of an object may also depend on what other objects
are available as alternatives. In this paper, we formalize the problem of
context-dependent ranking and present two general approaches based on two
natural representations of context-dependent ranking functions. Both approaches
are instantiated by means of appropriate neural network architectures, which
are evaluated on suitable benchmark task.}},
  author       = {{Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:1803.05796}},
  title        = {{{Deep Architectures for Learning Context-dependent Ranking Functions}}},
  year         = {{2018}},
}

@inproceedings{19868,
  author       = {{Camberg, Alan Adam and Tröster, Thomas and Sotirov, Nikolay and Tölle, Jörn and Bohner, Friedrich}},
  booktitle    = {{Materials Science and Engineering (MSE) Congress 2018}},
  location     = {{Darmstadt}},
  title        = {{{Investigation of ductility and damage characteristics of EN AW-5182 H18 at non-isothermal forming conditions}}},
  year         = {{2018}},
}

@article{20,
  abstract     = {{Approximate computing has shown to provide new ways to improve performance
and power consumption of error-resilient applications. While many of these
applications can be found in image processing, data classification or machine
learning, we demonstrate its suitability to a problem from scientific
computing. Utilizing the self-correcting behavior of iterative algorithms, we
show that approximate computing can be applied to the calculation of inverse
matrix p-th roots which are required in many applications in scientific
computing. Results show great opportunities to reduce the computational effort
and bandwidth required for the execution of the discussed algorithm, especially
when targeting special accelerator hardware.}},
  author       = {{Lass, Michael and Kühne, Thomas and Plessl, Christian}},
  issn         = {{1943-0671}},
  journal      = {{Embedded Systems Letters}},
  number       = {{2}},
  pages        = {{ 33--36}},
  publisher    = {{IEEE}},
  title        = {{{Using Approximate Computing for the Calculation of Inverse Matrix p-th Roots}}},
  doi          = {{10.1109/LES.2017.2760923}},
  volume       = {{10}},
  year         = {{2018}},
}

@article{3510,
  abstract     = {{Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.}},
  author       = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  issn         = {{1573-0565}},
  journal      = {{Machine Learning}},
  keywords     = {{AutoML, Hierarchical Planning, HTN planning, ML-Plan}},
  location     = {{Dublin, Ireland}},
  pages        = {{1495--1515}},
  publisher    = {{Springer}},
  title        = {{{ML-Plan: Automated Machine Learning via Hierarchical Planning}}},
  doi          = {{10.1007/s10994-018-5735-z}},
  year         = {{2018}},
}

@unpublished{3586,
  abstract     = {{Existing approaches and tools for the generation of approximate circuits often lack generality and are restricted to certain circuit types, approximation techniques, and quality assurance methods. Moreover, only few tools are publicly available. This hinders the development and evaluation of new techniques for approximating circuits and their comparison to previous approaches. In this paper, we first analyze and classify related approaches and then present CIRCA, our flexible framework for search-based approximate circuit generation. CIRCA is developed with a focus on modularity and extensibility. We present the architecture of CIRCA with its clear separation into stages and functional blocks, report on the current prototype, and show initial experiments.}},
  author       = {{Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco}},
  booktitle    = {{Third Workshop on Approximate Computing (AxC 2018)}},
  keywords     = {{Approximate Computing, Framework, Pareto Front, Accuracy}},
  pages        = {{6}},
  title        = {{{CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}}},
  year         = {{2018}},
}

@inproceedings{3588,
  abstract     = {{In scientific computing, unstructured meshes are a crucial foundation for the simulation of real-world physical phenomena. Compared to regular grids, they allow resembling the computational domain with a much higher accuracy, which in turn leads to more efficient computations.<br />There exists a wealth of supporting libraries and frameworks that aid programmers with the implementation of applications working on such grids, each built on top of existing parallelization technologies. However, many approaches require the programmer to introduce a different programming paradigm into their application or provide different variants of the code. SYCL is a new programming standard providing a remedy to this dilemma by building on standard C ++17 with its so-called single-source approach: Programmers write standard C ++ code and expose parallelism using C++17 keywords. The application is<br />then transformed into a concrete implementation by the SYCL implementation. By encapsulating the OpenCL ecosystem, different SYCL implementations enable not only the programming of CPUs but also of heterogeneous platforms such as GPUs or other devices. For the first time, this paper showcases a SYCL-<br />based solver for the nodal Discontinuous Galerkin method for Maxwell’s equations on unstructured meshes. We compare our solution to a previous C-based implementation with respect to programmability and performance on heterogeneous platforms.<br}},
  author       = {{Afzal, Ayesha and Schmitt, Christian and Alhaddad, Samer and Grynko, Yevgen and Teich, Jürgen and Förstner, Jens and Hannig, Frank}},
  booktitle    = {{Proceedings of the 29th Annual IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP)}},
  isbn         = {{978-1-5386-7479-6}},
  keywords     = {{tet_topic_hpc}},
  pages        = {{49--56}},
  title        = {{{Solving Maxwell's Equations with Modern C++ and SYCL: A Case Study}}},
  doi          = {{10.1109/ASAP.2018.8445127}},
  year         = {{2018}},
}

@unpublished{1165,
  author       = {{Witschen, Linus Matthias and Wiersema, Tobias and Platzner, Marco}},
  booktitle    = {{4th Workshop On Approximate Computing (WAPCO 2018)}},
  title        = {{{Making the Case for Proof-carrying Approximate Circuits}}},
  year         = {{2018}},
}

@inproceedings{2109,
  abstract     = {{In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.}},
  author       = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}},
  keywords     = {{Classification, Hierarchical Decomposition, Indirect Encoding}},
  location     = {{Kyoto, Japan}},
  publisher    = {{ACM}},
  title        = {{{Ensembles of Evolved Nested Dichotomies for Classification}}},
  doi          = {{10.1145/3205455.3205562}},
  year         = {{2018}},
}

@unpublished{17713,
  author       = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  publisher    = {{Arxiv}},
  title        = {{{Automated Multi-Label Classification based on ML-Plan}}},
  year         = {{2018}},
}

@unpublished{17714,
  author       = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  title        = {{{Automated machine learning service composition}}},
  year         = {{2018}},
}

@inproceedings{23968,
  author       = {{Zibart, Alexander and Kenig, Eugeny}},
  location     = {{Beijing, China}},
  title        = {{{FALLING LIQUID FILM FLOW OVER THE WAVY SURFACE OF VERTICAL PILLOW PLATES − A NUMERICAL INVESTIGATION}}},
  doi          = {{10.1615/IHTC16.mpf.024081}},
  year         = {{2018}},
}

@inproceedings{23971,
  author       = {{Zibart, Alexander and Kenig, Eugeny}},
  location     = {{Bremen}},
  title        = {{{Numerische Untersuchung der gravitationsgetriebenen Filmströmung auf der welligen Oberfläche von Kissenplattenwärmeübertragern}}},
  year         = {{2018}},
}

@inproceedings{4581,
  author       = {{Grynko, Yevgen and Förstner, Jens}},
  booktitle    = {{2018 IEEE 17th International Conference on Mathematical Methods in Electromagnetic Theory (MMET)}},
  isbn         = {{9781538654385}},
  keywords     = {{tet_topic_numerics, tet_topic_shg}},
  publisher    = {{IEEE}},
  title        = {{{Application of the Discontinuous Galerkin Time Domain Method in Nonlinear Nanoplasmonics}}},
  doi          = {{10.1109/mmet.2018.8460261}},
  year         = {{2018}},
}

@inbook{8754,
  abstract     = {{In this chapter, we combine a global, derivative-free subdivision algorithm for multiobjective optimization problems with a posteriori error estimates for reduced-order models based on Proper Orthogonal Decomposition in order to efficiently solve multiobjective optimization problems governed by partial differential equations. An error bound for a semilinear heat equation is developed in such a way that the errors in the conflicting objectives can be estimated individually. The resulting algorithm constructs a library of locally valid reduced-order models online using a Greedy (worst-first) search. Using this approach, the number of evaluations of the full-order model can be reduced by a factor of more than 1000.}},
  author       = {{Beermann, Dennis and Dellnitz, Michael and Peitz, Sebastian and Volkwein, Stefan}},
  booktitle    = {{Reduced-Order Modeling (ROM) for Simulation and Optimization}},
  isbn         = {{9783319753188}},
  pages        = {{47--72}},
  title        = {{{Set-Oriented Multiobjective Optimal Control of PDEs Using Proper Orthogonal Decomposition}}},
  doi          = {{10.1007/978-3-319-75319-5_3}},
  year         = {{2018}},
}

@article{8755,
  abstract     = {{Dynamic mode decomposition (DMD) is a recently developed tool for the analysis of the behavior of complex dynamical systems. In this paper, we will propose an extension of DMD that exploits low-rank tensor decompositions of potentially high-dimensional data sets to compute the corresponding DMD modes and eigenvalues. The goal is to reduce the computational complexity and also the amount of memory required to store the data in order to mitigate the curse of dimensionality. The efficiency of these tensor-based methods will be illustrated with the aid of several different fluid dynamics problems such as the von Kármán vortex street and the simulation of two merging vortices.}},
  author       = {{Klus, Stefan and Gelß, Patrick and Peitz, Sebastian and Schütte, Christof}},
  issn         = {{0951-7715}},
  journal      = {{Nonlinearity}},
  number       = {{7}},
  pages        = {{3359--3380}},
  title        = {{{Tensor-based dynamic mode decomposition}}},
  doi          = {{10.1088/1361-6544/aabc8f}},
  volume       = {{31}},
  year         = {{2018}},
}

@inproceedings{8757,
  abstract     = {{A framework for set‐oriented multiobjective optimal control of partial differential equations using reduced order modeling has recently been developed [1]. Following concepts from localized reduced bases methods, error estimators for the reduced cost functionals are utilized to construct a library of locally valid reduced order models. This way, a superset of the Pareto set can efficiently be computed while maintaining a prescribed error bound. In this article, this algorithm is applied to a problem with non‐smooth objective functionals. Using an academic example, we show that the extension to non‐smooth problems can be realized in a straightforward manner. We then discuss the implications on the numerical results.}},
  author       = {{Beermann, Dennis and Dellnitz, Michael and Peitz, Sebastian and Volkwein, Stefan}},
  booktitle    = {{PAMM}},
  issn         = {{1617-7061}},
  pages        = {{51--54}},
  title        = {{{POD-based multiobjective optimal control of PDEs with non-smooth objectives}}},
  doi          = {{10.1002/pamm.201710015}},
  year         = {{2018}},
}

@article{6567,
  author       = {{Johannesmann, Sarah and Düchting, Julia and Webersen, Manuel and Claes, Leander and Henning, Bernd}},
  issn         = {{0171-8096}},
  journal      = {{tm - Technisches Messen}},
  keywords     = {{Continous-fibre reinforced plastics, material parameters, orthotropy, ultrasonics}},
  number       = {{85}},
  pages        = {{478--486}},
  title        = {{{An acoustic waveguide-based approach to the complete characterisation of linear elastic, orthotropic material behaviour}}},
  doi          = {{10.1515/teme-2017-0132}},
  volume       = {{2018}},
  year         = {{2018}},
}

@inproceedings{6568,
  author       = {{Johannesmann, Sarah and Brockschmidt, Tobias and Rump, Friedhelm and Webersen, Manuel and Claes, Leander and Henning, Bernd}},
  booktitle    = {{Sensoren und Messsysteme}},
  pages        = {{231--234}},
  publisher    = {{VDE Verlag GmbH}},
  title        = {{{Acoustic material characterization of prestressed, plate-shaped specimens}}},
  year         = {{2018}},
}

@article{6577,
  author       = {{Webersen, Manuel and Johannesmann, Sarah and Düchting, Julia and Claes, Leander and Henning, Bernd}},
  journal      = {{Ultrasonics}},
  pages        = {{53--62}},
  title        = {{{Guided ultrasonic waves for determining effective orthotropic material parameters of continuous-fiber reinforced thermoplastic plates}}},
  doi          = {{10.1016/j.ultras.2017.10.005}},
  volume       = {{84}},
  year         = {{2018}},
}

@inproceedings{6578,
  author       = {{Webersen, Manuel and Johannesmann, Sarah and Düchting, Julia and Claes, Leander and Henning, Bernd}},
  booktitle    = {{Fortschritte der Akustik - DAGA 2018}},
  pages        = {{1263--1266}},
  title        = {{{Akustische Charakterisierung der richtungsabhängigen elastischen Eigenschaften faserverstärkter Kunststoffe}}},
  year         = {{2018}},
}