151 citations · 367 across the 6 of their papers we have counts for
7 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…
PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
Brian M. de Silva, Kathleen Champion, Markus Quade +3
PySINDy is a Python package for the discovery of governing dynamical systems models from data. In particular, PySINDy provides tools for applying the sparse identification of nonli…
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…
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…