05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Institute: search.filters.UBSInstitut.Institut für Parallele und Verteilte SystemeSubject: search.filters.subject.620Start date: 2023End date: 2023

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    A preCICE-FMI Runner to couple controller models to PDEs
    (2023) Willeke, Leonard
    Partitioned simulation or co-simulation allows to simulate complex systems by breaking them into smaller, independent subsystems. The Functional Mock-Up Interface FMI enables co-simulation by defining a framework for simulation models. Models adhering to the standard interface (FMUs) are executed and coupled by an importer. This framework approach works well for models based on ODEs and DAEs but reaches its limits for models based on PDEs. Such models require sophisticated, legacy software packages not compatible with the FMI standard. However, only PDE-based models are able to accurately simulate many physical aspects important in engineering like heat transfer or Fluid-Structure interactions. A possible solution to this problem is the open-source coupling library preCICE. preCICE couples PDE-based simulation programs in a black-box fashion to achieve partitioned multi-physics simulations. The coupling of the FMI standard to preCICE would allow the co-simulation of FMI models with the more than 20 simulation programs in the preCICE ecosystem. This thesis is focused on the development of a preCICE-FMI Runner software to couple FMUs with preCICE. The Runner serves as an importer to execute the FMU and steer the simulation. Additionally, it calls the preCICE library to communicate and coordinate with other solvers. The scope is not to develop a general Runner software, but to couple FMUs that contain control algorithms with PDE-based models as a first step. The software is written in Python and relies on the Python package FMPy as well as the preCICE Python bindings. Two test cases show the functionality of the preCICE-FMI Runner. The coupling of ODE-based models with FMUs matches the results of a pure Python implementation with an accuracy of 10 −4 . The coupling of a PDE-based model to a controller FMU proofs the working principle, although the results could not be tested against other implementations. The scope of the implemented abilities restricts the possible simulation scenarios, but does not prohibit a general use for coupling scenarios beyond control applications.
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    AssistML : an approach to manage, recommend and reuse ML solutions
    (2023) Villanueva Zacarias, Alejandro Gabriel; Reimann, Peter; Weber, Christian; Mitschang, Bernhard
    The adoption of machine learning (ML) in organizations is characterized by the use of multiple ML software components. When building ML systems out of these software components, citizen data scientists face practical requirements which go beyond the known challenges of ML, e. g.,  data engineering or parameter optimization. They are expected to quickly identify ML system options that strike a suitable trade-off across multiple performance criteria. These options also need to be understandable for non-technical users. Addressing these practical requirements represents a problem for citizen data scientists with limited ML experience. This calls for a concept to help them identify suitable ML software combinations. Related work, e. g.,  AutoML systems, are not responsive enough or cannot balance different performance criteria. This paper explains how AssistML, a novel concept to recommend ML solutions, i. e.,  software systems with ML models, can be used as an alternative for predictive use cases. Our concept collects and preprocesses metadata of existing ML solutions to quickly identify the ML solutions that can be reused in a new use case. We implement AssistML  and evaluate it with two exemplary use cases. Results show that AssistML can recommend ML solutions in line with users’ performance preferences in seconds. Compared to AutoML, AssistML offers citizen data scientists simpler, intuitively explained ML solutions in considerably less time. Moreover, these solutions perform similarly or even better than AutoML models.