What is she best known for?
The fields of study she is best known for:
- Control theory
- Mathematical analysis
- Electrical engineering
The scientist’s investigation covers issues in Fractional calculus, Control theory, Mathematical analysis, Calculus and State-space representation.
Her Fractional calculus study results in a more complete grasp of Applied mathematics.
Her Applied mathematics research includes elements of Representation and Robustness.
The concepts of her Control theory study are interwoven with issues in State of charge, Operating temperature and Fractal.
Jocelyn Sabatier has researched Mathematical analysis in several fields, including Basis, State variable and State vector.
Jocelyn Sabatier interconnects Representation, State space and Displacement current in the investigation of issues within Calculus.
Her most cited work include:
- Advances in Fractional Calculus: Theoretical Developments and Applications in Physics and Engineering (864 citations)
- Advances in Fractional Calculus (844 citations)
- LMI stability conditions for fractional order systems (302 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Control theory, Applied mathematics, Crone, Control engineering and Fractional calculus.
Her Control theory study frequently involves adjacent topics like Frequency domain.
In general Applied mathematics, her work in Fractional-order system is often linked to Initialization linking many areas of study.
Her Crone research is multidisciplinary, relying on both Representation and Closed-loop transfer function.
Her Control engineering research integrates issues from Linear system, Electronic speed control and Fractional differentiation.
Her Fractional calculus study incorporates themes from Calculus, Integer and Stability conditions.
She most often published in these fields:
- Control theory (42.70%)
- Applied mathematics (17.98%)
- Crone (16.85%)
What were the highlights of her more recent work (between 2017-2020)?
- Power law (3.93%)
- Applied mathematics (17.98%)
- Control theory (42.70%)
In recent papers she was focusing on the following fields of study:
Jocelyn Sabatier spends much of her time researching Power law, Applied mathematics, Control theory, Type and Long memory.
Her work carried out in the field of Applied mathematics brings together such families of science as Volterra equations, Partial differential equation and Order.
Her Volterra equations study combines topics in areas such as Fractional differential, Fractional calculus, Current and Field.
Her work deals with themes such as Battery and Lithium, which intersect with Control theory.
Jocelyn Sabatier combines subjects such as Optoelectronics, Power density and Linearization with her study of Battery.
The study incorporates disciplines such as Transfer function, Distribution and Heat equation in addition to Type.
Between 2017 and 2020, her most popular works were:
- Comments on the description and initialization of fractional partial differential equations using Riemann–Liouville’s and Caputo’s definitions (16 citations)
- Direct 3D-printing of phosphate glass by fused deposition modeling (6 citations)
- A Fractional-Order Electro-Thermal Aging Model for Lifetime Enhancement of Lithium-ion Batteries (5 citations)
In her most recent research, the most cited papers focused on:
- Control theory
- Mathematical analysis
- Electrical engineering
Applied mathematics, Power law, Long memory, Volterra equations and Type are her primary areas of study.
Her studies deal with areas such as Field and Counterexample as well as Applied mathematics.
In her works, Jocelyn Sabatier undertakes multidisciplinary study on Power law and Fractional differentiation.
Jocelyn Sabatier has included themes like Heap, Pile and Mathematical optimization in her Fractional differentiation study.
Jocelyn Sabatier interconnects Current, Fractional calculus and Order in the investigation of issues within Volterra equations.
Type is intertwined with Class, Non singular and Field in her study.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
