A1 Journal article (refereed)
Hybrid modeling design patterns (2024)
Rudolph, M., Kurz, S., & Rakitsch, B. (2024). Hybrid modeling design patterns. Journal of Mathematics in Industry, 14, Article 3. https://doi.org/10.1186/s13362-024-00141-0
JYU authors or editors
Publication details
All authors or editors: Rudolph, Maja; Kurz, Stefan; Rakitsch, Barbara
Journal or series: Journal of Mathematics in Industry
eISSN: 2190-5983
Publication year: 2024
Publication date: 19/03/2024
Volume: 14
Article number: 3
Publisher: Springer
Publication country: Germany
Publication language: English
DOI: https://doi.org/10.1186/s13362-024-00141-0
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/94095
Web address of parallel published publication (pre-print): https://arxiv.org/abs/2401.00033
Abstract
Design patterns provide a systematic way to convey solutions to recurring modeling challenges. This paper introduces design patterns for hybrid modeling, an approach that combines modeling based on first principles with data-driven modeling techniques. While both approaches have complementary advantages there are often multiple ways to combine them into a hybrid model, and the appropriate solution will depend on the problem at hand. In this paper, we provide four base patterns that can serve as blueprints for combining data-driven components with domain knowledge into a hybrid approach. In addition, we also present two composition patterns that govern the combination of the base patterns into more complex hybrid models. Each design pattern is illustrated by typical use cases from application areas such as climate modeling, engineering, and physics.
Keywords: planning and design; modelling (representation); models (objects); artificial intelligence
Free keywords: hybrid modeling; physics-inspired AI; design patterns
Contributing organizations
Ministry reporting: Yes
Preliminary JUFO rating: 1
