Probabilistic Behavioral Distance and Tuning - Reducing and aggregating complex systems

arXiv2111.12521MaRDI QIDQ6383862

Author name not available (Why is that?)

Publication date: 24 November 2021

Abstract: Given a complex system with a given interface to the rest of the world, what does it mean for a the system to behave close to a simpler specification describing the behavior at the interface? We give several definitions for useful notions of distances between a complex system and a specification by combining a behavioral and probabilistic perspective. These distances can be used to tune a complex system to a specification. We show that our approach can successfully tune non-linear networked systems to behave like much smaller networks, allowing us to aggregate large sub-networks into one or two effective nodes. Finally, we discuss similarities and differences between our approach and $H_{i}nfty$ model reduction.

Has companion code repository: https://github.com/pik-icone/probetune.jl

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