3 papers
cs.LG2021
Concepts for Automated Machine Learning in Smart Grid Applications
Stefan Meisenbacher, Janik Pinter, Tim Martin +2
Undoubtedly, the increase of available data and competitive machine learning algorithms has boosted the popularity of data-driven modeling in energy systems. Applications are forec…
eess.SY2020
Dissipativity verification with guarantees for polynomial systems from noisy input-state data
Tim Martin, Frank Allgöwer
In this paper, we investigate the verification of dissipativity properties for polynomial systems without an explicitly identified model but directly from noise-corrupted measureme…
eess.SY2020
Iterative data-driven inference of nonlinearity measures via successive graph approximation
Tim Martin, Frank Allgöwer
In this paper, we establish an iterative data-driven approach to derive guaranteed bounds on nonlinearity measures of unknown nonlinear systems. In this context, nonlinearity measu…