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STRUCTURED - Modelling structured dynamical systems: parametric and non-parametric approaches

From 01-01-2012 to 31-12-2015

Description

The aim of this project “Modelling structured dynamical systems: parametric and non-parametric approaches” is to realize synergies between different families of modelling and identification techniques for structured nonlinear dynamical systems. A first class of methodologies relates to support vector machines, originally developed within the machine learning community. A second class is based on classical system identification, which finds its roots mainly within the systems and control community. Parametric and non-parametric approaches will be studied from these different perspectives. This project aims at achieving complementary and unifying insights with:- a better understanding of the impact of the intrinsic assumptions, e.g. how crucial is it to impose certain models structures in predictive modelling?- a better understanding of the strong and weak points of the different approaches: why is one approach successful on certain types of problems while it may totally fail on others?- proposing new methods that combine and integrate the best of different modelling paradigms- data driven detection of the dynamic structure of the nonlinear system

Team

Financing

Funding: FWO - Research Foundation - Flanders

Program/Grant Type: FWO Research Grant - FWO Research Grant

Events

2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery


3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery


12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers


More events

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