Knowledge Amalgamation for Computational Science and Engineering
In: Lecture Notes in Computer Science ISBN: 9783319968117 CICM; (2018)
Online
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Zugriff:
This paper addresses a knowledge gap that is commonly encountered in computational science and engineering: To set up a simulation, we need to combine domain knowledge (usually in terms of physical principles), model knowledge (e.g. about suitable partial differential equations) with simulation (i.e. numerics/computing) knowledge. In current practice, this is resolved by intense collaboration between experts, which incurs non-trivial translation and communication overheads. We propose an alternate solution, based on mathematical knowledge management (MKM) techniques, specifically theory graphs and active documents: Given a theory graph representation of the domain, model, and background mathematics, we can derive a targeted knowledge acquisition dialogue that supports the formalization of domain knowledge, combines it with simulation knowledge and – in the end – drives a simulation run – a process we call MoSIS (“Models-to-Simulations Interface System”). We present the MoSIS prototype that implements this process based on a custom Jupyter kernel for the user interface and the theory-graph-based Mmt knowledge management system as an MKM backend.
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Knowledge Amalgamation for Computational Science and Engineering
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Autor/in / Beteiligte Person: | Kohlhase, Michael ; Pollinger, Theresa ; Köstler, Harald |
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Quelle: | Lecture Notes in Computer Science ISBN: 9783319968117 CICM; (2018) |
Veröffentlichung: | Springer International Publishing, 2018 |
Medientyp: | unknown |
ISBN: | 978-3-319-96811-7 (print) |
DOI: | 10.1007/978-3-319-96812-4_20 |
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