295 citations · 968 across the 13 of their papers we have counts for
14 papers
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference
Robert K. Niven, Laurent Cordier, Ali Mohammad-Djafari +2
This study presents a Bayesian maximum \textit{a~posteriori} (MAP) framework for dynamical system identification from time-series data. This is shown to be equivalent to a generali…
Explainable Machine Learning Control -- robust control and stability analysis
Markus Quade, Thomas Isele, Markus Abel
Recently, the term explainable AI became known as an approach to produce models from artificial intelligence which allow interpretation. Since a long time, there are models of symb…
Inferring the dynamics of oscillatory systems using recurrent neural networks
Rok Cestnik, Markus Abel
We investigate the predictive power of recurrent neural networks for oscillatory systems not only on the attractor, but in its vicinity as well. For this we consider systems pertur…
Glyph: Symbolic Regression Tools
Markus Quade, Julien Gout, Markus Abel
We present Glyph - a Python package for genetic programming based symbolic regression. Glyph is designed for usage let by numerical simulations let by real world experiments. For e…
Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery
Markus Quade, Markus Abel, J. Nathan Kutz +1
Big data has become a critically enabling component of emerging mathematical methods aimed at the automated discovery of dynamical systems, where first principles modeling may be i…
Learning Optimal Control of Synchronization in Networks of Coupled Oscillators using Genetic Programming-based Symbolic Regression
Julien Gout, Markus Quade, Kamran Shafi +2
Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networke…