The rocky road to extended simulation frameworks covering uncertainty, inversion, optimization and control

摘要

In the past decades, simulation frameworks have greatly increased in complexity, due to coupling of models from various disciplines into so-called integrated models. Recently, the combination with tools for uncertainty quantification, inverse modelling, optimization and control started a development towards what we call extended simulation frameworks. While there is an ongoing discussion on quality assurance and reproducibility for simulation frameworks, we have not observed a similar discussion for the extended case. Particularly for extended frameworks, the need for quality assurance is high: The overwhelming range of options and algorithms is unmanageable by a domain expert and opaque to decision makers or the public. The resulting demand for 'intelligent software' with automated configuration can lead to a blind trust in simulation results even if they are incorrect. This is a threatening scenario due to potential consequences in simulation-based engineering or political decisions. In this paper, we analyze the increasing complexity of scientific computing workflows, and discuss the corresponding problems of extended scientific simulation frameworks. We propose a paradigm that regulates the allowable properties of framework components, supports the framework configuration for complex simulations, enforces automatic self-tests of configured frameworks, and communicates automated algorithm choices, potentially critical user settings or convergence issues with adaptive detail level and urgency to the end-user. Our goal is to start transferring the quality assurance discussion in the field of integrated modeling and conventional software frameworks to the area of extended simulation frameworks. With this, we hope to increase the reliability and transparency of (extended) frameworks, framework use and of the corresponding simulation results. (C) 2017 Elsevier Ltd. All rights reserved.