Provenance-integrated adaptation of numerical approximations of differential equation models

PN 7-3 (II)

Project description

We explore how to leverage metadata and provenance collected a priori and during the execution of a simulation of a differential equation model, with the goal of using these to adapt, improve, and predict the simulation. Such metadata include ‘low-level’ performance metrics obtained by monitoring, e.g., convergence rates, runtime, and memory consumption. By analyzing such provenance relying on data mining, knowledge discovery, and machine learning methods and combining it with mathematical and domain knowledge, further potentially more costly ‘high-level’ measures and metrics can be derived that enable predictions of the estimated difficulty of a solution or the similarity of tasks and suitability of components in different simulations.

We continue to study two fundamental problems: deciding when a (possibly expensive) provenance capture enables more informed design decisions; and adapting parameter settings to improve performance. This requires the investigation of methods and measures defining high-level provenance and algorithms leveraging it, as well as execution strategies of a. We also plan to explore provenance management and use when multiple simulations are involved. The results of this project inform multi-adaptive simulations.

Project information

Project title Provenance-integrated adaption of numerical approximations of differential equation models
Project Leader Melanie Herschel (Dominik Göddeke)
Project duration July 2022 - December 2025
Project number PN 7-3 (II)

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.
  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.
To the top of the page