activity
20042024
most citedPrediction of Dynamical Systems by Symbolic Regression

142 citations · 456 across the 24 of their papers we have counts for

collaborators

24 papers

stat.ME2024★ 20 cited

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…

physics.flu-dyn2023

Dimensionless Groups by Entropic Similarity

Robert K. Niven

We propose an additional category of dimensionless groups based on the principle of {\it entropic similarity}, defined by ratios of (i) entropy production terms; (ii) entropy flow…

physics.flu-dyn2021★ 3 cited

New Classes of Conservation Laws Based on Generalized Fluid Densities and Reynolds Transport Theorems

Robert K. Niven

The Reynolds transport theorem occupies a central place in fluid dynamics, providing a generalized integral conservation equation for the transport of any conserved quantity within…

physics.flu-dyn2018★ 3 cited

Rethinking the Reynolds Transport Theorem, Liouville Equation, and Perron-Frobenius and Koopman Operators

Robert K. Niven, Laurent Cordier, Eurika Kaiser +2

The Reynolds transport theorem provides a generalized conservation law for the transport of a conserved quantity by fluid flow through a continuous connected control volume. It is…

nlin.AO2016★ 17 cited

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…

physics.data-an2016★ 142 cited

Prediction of Dynamical Systems by Symbolic Regression

Markus Quade, Markus Abel, Kamran Shafi +2

We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the under…