Objectif
The proposed research aims to establish groundbreaking new computational methods for the analysis of deterministic, control and random dynamical systems by using tools from the field of Statistical Learning Theory (a.k.a. Machine Learning). These three related system types constitute a very active research area with a wide range of applications, including climate studies, power systems, autonomous vehicle control, aircraft control, robotics, finance and chemical engineering. In many applications the understanding of such systems requires the availability of efficient, high-performance algorithms and a key innovative aspect of this proposal is the development of a unified theory for computational dynamics that has the potential to establish a new field at the intersection of machine learning and dynamical systems theory.
Champ scientifique
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- natural sciencesmathematicsapplied mathematicsdynamical systems
- natural sciencescomputer and information sciencescomputational science
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Régime de financement
MSCA-IF-EF-ST - Standard EFCoordinateur
SW7 2AZ LONDON
Royaume-Uni