SOURCE-LINKED INTELLIGENCE

Kernel-based Methods in Control and Estimation

CORDIS · observation · Publication date unknown

as state estimation for partial differential equations, data-driven analysis of nonlinear dynamics, and uncertainty quantification in complex systems. The network will also explore the integration of machine learning methods, including physics-informed deep learning and Gaussian process online learning, to enhance control strategies and state estimation in uncertain and nonlinear environments. Additionally, the project will address real-world applications from different technological domains. Furthermore, by incorporating physical prior-knowledge into data-driven control and developing localized sensitivities for efficient learning, the network seeks to create robust, reliable, and efficient methods applicable across various industries. The doctoral network will not only contribute to advancing mathematical knowledge but also strengthen European innovation capacity by training highly skilled researchers equipped to tackle complex mathematical problems. Through collaborative research projects, workshops, and dissemination of findings in top-tier journals and conferences, the network w

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

4592711.88

unit

EUR