Provenance-integrated adaptation of numerical approximations of differential equation models

PN 7-3

Project description

This project explores how to leverage metadata collected a priori and during the execution of a simulation of a differential equation model, with the goal of using these metadata to adapt, improve and predict the simulation. Such metadata, commonly referred to as provenance, include ‘low-level’ performance metrics obtained by monitoring convergence rate, runtime, or memory consumption as well as novel ‘high-level’ measures and derived metrics that help quantify the estimated difficulty of a solution, or the similarity of tasks / components in different simulations. We will contribute novel methods and measures to capture these metadata as well as corresponding analysis algorithms to ultimately advance the fundamental problems of deciding when a (possibly expensive) provenance capture is useful to improve the overall performance or to enable more informed design decisions; and of adapting parameter settings to a given problem. The results of this project will pave the way towards multi-adaptive simulations, in particular in project networks 5 and 7. Furthermore, the project delivers input for SimTech's openDASH data and software hub, and thus contributes towards reproducibility and traceability of simulations.

Project information

Project title Provenance-integrated adaption of numerical approximations of differential equation models
Project leaders Dominik Göddeke (Melanie Herschel)
Project partners Frank Leymann
Miriam Schulte
Kurt Rothermel
Oliver Röhrle
Project duration June 2020 - June 2023
Project number PN 7-3

Publications PN 7-3 and PN 7-3 (II)

  1. 2024

    1. J. Meißner, D. Göddeke, and M. Herschel, Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations. in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). 2024.
    2. B. Flemisch et al., “Research Data Management in Simulation Science: Infrastructure, Tools, and Applications,” Datenbank-Spektrum, 2024, doi: https://doi.org/10.1007/s13222-024-00475-4.

  2. 2022

    1. S. Oppold and M. Herschel, “Provenance-based explanations: are they useful?,” in International Workshop on the Theory and Practice  of Provenance (TAPP), in International Workshop on the Theory and Practice  of Provenance (TAPP). 2022, pp. 2:1--2:4. doi: 10.1145/3530800.3534529.

  3. 2021

    1. R. Diestelkämper, S. Lee, M. Herschel, and B. Glavic, “To not miss the forest for the trees - A holistic approach for explaining missing answers over nested data,” in In Proceedings of the ACM SIG Conference on the Management of Data (SIGMOD), in In Proceedings of the ACM SIG Conference on the Management of Data (SIGMOD). 2021.
    2. J. Kühnert, D. Göddeke, and M. Herschel, “Provenance-integrated parameter selection and optimization in numerical simulations,” in International Workshop on the Theory and Practice of Provenance (TAPP), in International Workshop on the Theory and Practice of Provenance (TAPP). USENIX Association, 2021.

  4. 2020

    1. R. Diestelkämper and M. Herschel, “Tracing nested data with structural provenance for big data analytics,” in Proceedings of the International Conference on Extending Database Technology (EDBT), in Proceedings of the International Conference on Extending Database Technology (EDBT). 2020, pp. 253–264. doi: 10.5441/002/edbt.2020.23.
    2. R. Diestelkämper and M. Herschel, “Distributed Tree-Pattern Matching in Big Data Analytics Systems,” in In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS), in In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS). Springer, 2020, pp. 171–186. doi: https://doi.org/10.1007/978-3-030-54832-2_14.
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