Delay-differential-algebraic equations in control theory

By Lawrence F. Shampine, P. Gahinet. 2006

52 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Numerical Analysis, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b9137a7dfd9e6f7217e7a8e23be545fd383edad5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Marino Zennaro
2. Nicola Guglielmi
3. Qing Nie
4. Christopher Rackauckas

In Numerical Analysis

1. Nicola Guglielmi
2. Stefano Maset
3. Marino Zennaro
4. Alfredo Bellen
5. Qing Nie
6. Christopher Rackauckas
7. Christopher T. H. Baker
8. Evelyn Buckwar

In Statistical and Nonlinear Physics

1. Qing Nie
2. Christopher Rackauckas

Citations in the corpus

• DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia (2017, methodology): “Delay differential equations have become a ubiquitous tool for modeling phenomena with natural delays as seen in Neuroscience [3, 22] and control theory [24].”
• Recent trends in the numerical solution of retarded functional differential equations* (2009, background): “, the models in Shampine and Gahinet (2006)).”
• Effective connectivity: Influence, causality and biophysical modeling (2011, unknown): “This approach (of including algebraic constraints) affords the possibility of building complex models from nonlinear components, using simple interconnection rules—something that has been developed for control theory (Shampine and Gahinet, 2006).”