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 Number PN 7-3
Project Name Provenance-integrated adaption of numerical approximations of differential equation models
Project Duration June 2020 - June 2023
Project Leader Dominik Göddeke
Melanie Herschel (Collaborator)
Project Members N.N.
Project Partners Frank Leymann
Miriam Mehl
Kurt Rothermel
Oliver Röhrle
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