Provenance-integrated adaptation of numerical approximations of differential equation models


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 PN7-3
Project Name Provenance-integrated adaption of numerical approximations of differential equation models
Project Duration 3.5 years
Project Leader Dominik Göddeke
Melanie Herschel
Project Members N.N.
Project Partners Frank Leymann
Miriam Mehl
Kurt Rothermel
Oliver Röhrle
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