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ICFDT5 - 5th International Conference on Frontier in Diagnostic Technologies

3–5 Oct 2018
INFN-LNF, Italy
Europe/Rome timezone

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Session

Data Mining

4 Oct 2018, 16:30
Bruno Touschek Auditorium (INFN-LNF, Italy)

Bruno Touschek Auditorium

INFN-LNF, Italy

Via E. Fermi, 40 I-00044 Frascati

Conveners

Data Mining

  • Andrea Murari (Consorzio RFX)

Presentation materials

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  1. 12. Statistical learning theory for scientific applications: an overview
    Dr JESUS VEGA (Laboratorio Nacional de Fusión. CIEMAT)
    04/10/2018, 16:30
    Talk
    The statistical learning theory allows the estimation of functional dependency from a given collection of data. It includes discriminant analysis, regression analysis and the density estimation problem. It is used to obtain data-driven models to relate heterogeneous quantities with the aim of making predictions. In science, statistical learning is an essential theory to find relationships...
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  2. 3. A Model Falsification Approach to Learning in Non-Stationary Environments for Experimental Design
    Dr Andrea Murari (RFX Consortium and PMU)
    04/10/2018, 16:50
    Talk
    The application of machine learning and advanced statistical tools to complex physics experiments becomes very problematic, when the i.i.d. (independent and identical distribution) hypothesis is not verified, due the varying conditions of the systems to be studied. In particular, new experiments have to be executed in unexplored regions of the operational space. As a consequence, the input...
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  3. 27. Adaptive Learning for Disruption Prediction
    Dr Michela Gelfusa (Department of Industrial Engineering, University of Rome “Tor Vergata”, via del Politecnico 1, Roma, Italy)
    04/10/2018, 17:10
    Talk
    Accurate prediction of catastrophic events is becoming an important area of investigation in many research fields. In Tokamaks, detecting disruptions with sufficient anticipation time is a prerequisite to undertaking any remedial strategy, either for mitigation or for avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but tend...
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  4. 7. Causality detection methods for time series analysis
    Dr Teddy Craciunescu (National Institute for Lasers, Plasma and Raadiation Physics, Bucharest, Romania)
    04/10/2018, 17:30
    Talk
    Coupling and synchronization are common phenomena that occur in nature, e.g. in biological, physiological and environmental systems, as well as in physics and engineered systems. Depending on the coupling strength, the systems may undergo phase synchronization, generalized synchronization, lag synchronization and complete synchronization [1]. Lag or intermittent lag synchronization, where the...
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  5. 26. Determining the causality horizon in synchronization experiments
    Dr Emmanuele Peluso (University of Rome Tor Vergata)
    04/10/2018, 17:50
    Talk
    Synchronization can be defined as the coordination of events to operate a system in harmony. It is important in the operation of manmade systems and in the investigation of natural events. In the last decade or so, synchronization of multiple interacting dynamical systems has become a lively field of study for control. In Magnetic Confinement Nuclear Fusion, various pacing concepts have been...
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