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  1. Book ; Thesis: Einfluss von Prionen-Dekontaminationsprotokollen auf die Schneidleistung und Fraktursicherheit von endodontischen Nickel-Titan-Instrumenten des Mtwo-Systems

    Schmitt, Sebastian

    2010  

    Author's details vorgelegt von Sebastian Schmitt
    Language German
    Size III, 77 S. : Ill., graph. Darst., 21 cm
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Marburg, Univ., Diss., 2010
    HBZ-ID HT016769469
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2011 D 492 order for loan Magazin

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  2. Article ; Online: Data scheme and data format for transferable force fields for molecular simulation.

    Kanagalingam, Gajanan / Schmitt, Sebastian / Fleckenstein, Florian / Stephan, Simon

    Scientific data

    2023  Volume 10, Issue 1, Page(s) 495

    Abstract: A generalized data scheme for transferable classical force fields used in molecular simulations, i.e. molecular dynamics and Monte Carlo simulation, is presented. The data scheme is implemented in an SQL-based data format. The data scheme and data format ...

    Abstract A generalized data scheme for transferable classical force fields used in molecular simulations, i.e. molecular dynamics and Monte Carlo simulation, is presented. The data scheme is implemented in an SQL-based data format. The data scheme and data format is machine readable, re-usable, and interoperable. A transferable force field is a chemical construction plan specifying intermolecular and intramolecular interactions between different types of atoms or different chemical groups and can be used for building a model for a given component. The data scheme proposed in this work (named TUK-FFDat) formalizes digitally these chemical construction plans, i.e. transferable force fields. It can be applied to all-atom as well as united-atom transferable force fields. The general applicability of the data scheme is demonstrated for different types of force fields (TraPPE, OPLS-AA, and Potoff). Furthermore, conversion tools for translating the data scheme between .xls spread sheet format and the SQL-based data format are provided. The data format can readily be integrated in existing workflows, simulation engines, and force field databases as well as for linking such.
    MeSH term(s) Databases, Factual ; Molecular Dynamics Simulation ; Monte Carlo Method
    Language English
    Publishing date 2023-07-27
    Publishing country England
    Document type Dataset ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2775191-0
    ISSN 2052-4463 ; 2052-4463
    ISSN (online) 2052-4463
    ISSN 2052-4463
    DOI 10.1038/s41597-023-02369-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Comparison of Force Fields for the Prediction of Thermophysical Properties of Long Linear and Branched Alkanes.

    Schmitt, Sebastian / Fleckenstein, Florian / Hasse, Hans / Stephan, Simon

    The journal of physical chemistry. B

    2023  Volume 127, Issue 8, Page(s) 1789–1802

    Abstract: The prediction of thermophysical properties at extreme conditions is an important application of molecular simulations. The quality of these predictions primarily depends on the quality of the employed force field. In this work, a systematic comparison ... ...

    Abstract The prediction of thermophysical properties at extreme conditions is an important application of molecular simulations. The quality of these predictions primarily depends on the quality of the employed force field. In this work, a systematic comparison of classical transferable force fields for the prediction of different thermophysical properties of alkanes at extreme conditions, as they are encountered in tribological applications, was carried out using molecular dynamics simulations. Nine transferable force fields from three different classes were considered (all-atom, united-atom, and coarse-grained force fields). Three linear alkanes (
    Language English
    Publishing date 2023-02-20
    Publishing country United States
    Document type Journal Article
    ISSN 1520-5207
    ISSN (online) 1520-5207
    DOI 10.1021/acs.jpcb.2c07997
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  4. Article ; Online: Quantum annealing for industry applications: introduction and review.

    Yarkoni, Sheir / Raponi, Elena / Bäck, Thomas / Schmitt, Sebastian

    Reports on progress in physics. Physical Society (Great Britain)

    2022  Volume 85, Issue 10

    Abstract: Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum ... ...

    Abstract Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the QA algorithm for programmable use. Specifically, QA processors produced by D-Wave systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for QA as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of QA technology. We identify the advantages, limitations, and potential of QA for both researchers and practitioners from various fields.
    Language English
    Publishing date 2022-09-21
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 205657-4
    ISSN 1361-6633 ; 0034-4885
    ISSN (online) 1361-6633
    ISSN 0034-4885
    DOI 10.1088/1361-6633/ac8c54
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    In stock of ZB MED Bonn / Germany

    Z 52/38: Show issues

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  5. Article ; Online: Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times.

    Wang, Xilu / Jin, Yaochu / Schmitt, Sebastian / Olhofer, Markus

    Evolutionary computation

    2022  Volume 30, Issue 2, Page(s) 221–251

    Abstract: Most existing multiobjective evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is untenable in many real-world optimization scenarios where evaluation of ... ...

    Abstract Most existing multiobjective evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is untenable in many real-world optimization scenarios where evaluation of different objectives involves different computer simulations or physical experiments with distinct time complexity. To address this issue, a transfer learning scheme based on surrogate-assisted evolutionary algorithms (SAEAs) is proposed, in which a co-surrogate is adopted to model the functional relationship between the fast and slow objective functions and a transferable instance selection method is introduced to acquire useful knowledge from the search process of the fast objective. Our experimental results on DTLZ and UF test suites demonstrate that the proposed algorithm is competitive for solving bi-objective optimization where objectives have non-uniform evaluation times.
    MeSH term(s) Algorithms ; Biological Evolution ; Computer Simulation ; Learning ; Machine Learning
    Language English
    Publishing date 2022-04-07
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2022147-2
    ISSN 1530-9304 ; 1063-6560
    ISSN (online) 1530-9304
    ISSN 1063-6560
    DOI 10.1162/evco_a_00300
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  6. Book ; Online: Concept Identification for Complex Engineering Datasets

    Lanfermann, Felix / Schmitt, Sebastian

    2022  

    Abstract: Finding meaningful concepts in engineering application datasets which allow for a sensible grouping of designs is very helpful in many contexts. It allows for determining different groups of designs with similar properties and provides useful knowledge ... ...

    Abstract Finding meaningful concepts in engineering application datasets which allow for a sensible grouping of designs is very helpful in many contexts. It allows for determining different groups of designs with similar properties and provides useful knowledge in the engineering decision making process. Also, it opens the route for further refinements of specific design candidates which exhibit certain characteristic features. In this work, an approach to define meaningful and consistent concepts in an existing engineering dataset is presented. The designs in the dataset are characterized by a multitude of features such as design parameters, geometrical properties or performance values of the design for various boundary conditions. In the proposed approach the complete feature set is partitioned into several subsets called description spaces. The definition of the concepts respects this partitioning which leads to several desired properties of the identified concepts. This cannot be achieved with state-of-the-art clustering or concept identification approaches. A novel concept quality measure is proposed, which provides an objective value for a given definition of concepts in a dataset. The usefulness of the measure is demonstrated by considering a realistic engineering dataset consisting of about 2500 airfoil profiles, for which the performance values (lift and drag) for three different operating conditions were obtained by a computational fluid dynamics simulation. A numerical optimization procedure is employed, which maximizes the concept quality measure and finds meaningful concepts for different setups of the description spaces, while also incorporating user preference. It is demonstrated how these concepts can be used to select archetypal representatives of the dataset which exhibit characteristic features of each concept.

    Comment: 19 pages, 14 figures, accepted at Advanced Engineering Informatics
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-06-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces

    Lanfermann, Felix / Schmitt, Sebastian / Wollstadt, Patricia

    2023  

    Abstract: Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, ... ...

    Abstract Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.

    Comment: 10 pages, 6 figures, published in proceedings of 2022 IEEE International Conference on Data Mining Workshops (ICDMW)
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-01-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions

    Lanfermann, Felix / Liu, Qiqi / Jin, Yaochu / Schmitt, Sebastian

    2023  

    Abstract: Optimizing building configurations for an efficient use of energy is increasingly receiving attention by current research and several methods have been developed to address this task. Selecting a suitable configuration based on multiple conflicting ... ...

    Abstract Optimizing building configurations for an efficient use of energy is increasingly receiving attention by current research and several methods have been developed to address this task. Selecting a suitable configuration based on multiple conflicting objectives, such as initial investment cost, recurring cost, robustness with respect to uncertainty of grid operation is, however, a difficult multi-criteria decision making problem. Concept identification can facilitate a decision maker by sorting configuration options into semantically meaningful groups (concepts), further introducing constraints to meet trade-off expectations for a selection of objectives. In this study, for a set of 20000 Pareto-optimal building energy management configurations, resulting from a many-objective evolutionary optimization, multiple concept identification iterations are conducted to provide a basis for making an informed investment decision. In a series of subsequent analysis steps, it is shown how the choice of description spaces, i.e., the partitioning of the features into sets for which consistent and non-overlapping concepts are required, impacts the type of information that can be extracted and that different setups of description spaces illuminate several different aspects of the configuration data - an important aspect that has not been addressed in previous work.

    Comment: 16 pages, 7 figures, submitted to Applied Energy
    Keywords Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Systems and Control
    Subject code 006
    Publishing date 2023-06-14
    Publishing country us
    Document type Book ; Online
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  9. Article ; Online: Extension of the

    Schmitt, Sebastian / Kanagalingam, Gajanan / Fleckenstein, Florian / Froescher, Daniel / Hasse, Hans / Stephan, Simon

    Journal of chemical information and modeling

    2023  Volume 63, Issue 22, Page(s) 7148–7158

    Abstract: ... ...

    Abstract MolMod
    MeSH term(s) Molecular Dynamics Simulation ; Databases, Factual
    Language English
    Publishing date 2023-11-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c01484
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1230: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  10. Book ; Online: Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware

    Kaiser, Jakob / Stock, Raphael / Müller, Eric / Schemmel, Johannes / Schmitt, Sebastian

    2023  

    Abstract: The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons. When replicating neuroscientific experiment results, a major challenge is finding ... ...

    Abstract The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons. When replicating neuroscientific experiment results, a major challenge is finding suitable model parameters. This study investigates the suitability of the sequential neural posterior estimation (SNPE) algorithm for parameterizing a multi-compartmental neuron model emulated on the BSS-2 analog neuromorphic hardware system. In contrast to other optimization methods such as genetic algorithms or stochastic searches, the SNPE algorithms belongs to the class of approximate Bayesian computing (ABC) methods and estimates the posterior distribution of the model parameters; access to the posterior allows classifying the confidence in parameter estimations and unveiling correlation between model parameters. In previous applications, the SNPE algorithm showed a higher computational efficiency than traditional ABC methods. For our multi-compartmental model, we show that the approximated posterior is in agreement with experimental observations and that the identified correlation between parameters is in agreement with theoretical expectations. Furthermore, we show that the algorithm can deal with high-dimensional observations and parameter spaces. These results suggest that the SNPE algorithm is a promising approach for automating the parameterization of complex models, especially when dealing with characteristic properties of analog neuromorphic substrates, such as trial-to-trial variations or limited parameter ranges.
    Keywords Computer Science - Neural and Evolutionary Computing
    Subject code 006
    Publishing date 2023-03-28
    Publishing country us
    Document type Book ; Online
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