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The program 2:00 pm | Keynote lecture on the use of AI in research - Hugues BERSINI, Professor at the Université libre de Bruxelles: "Can science be just data driven?" 3:00 pm | Presentations by UNamur researchers3:00 pm | Catherine Guirkinger: Use of AI in an economic history project3:15 pm | Nicolas Roy (PI: Alexandre Mayer): AI at the service of innovation in photonics and optics: revealing the secrets of scrolls through the classification of animal species15:25 | Nemanja Antonic (PI: Elio Tuci): An in silico representation of C. elegans collective behaviour<15h35 | Nicolas Franco : The benefits and dangers of "predicting the future" with covid-like machine learning models 15h45 | Michel Ajzen : Managerial and human implications of AI in organizations <15h55 | Robin Ghyselinck (PI : Bruno Dumas) : Deep Learning for endoscopy: towards next generation computer-aided diagnosis4:05 pm | Auguste Debroise (PI : Guilhem Cassan) : LLMs to measure the importance of stereotypes within gender representations in Hollywood films16h15 | Gabriel Dias De Carvalho : Learning practices in physics using generative AI16h25 | Sébastien Dujardin (PI : Catherine Linard) : Where Geography meets AI: A case study on mapping online flood conversations16h35 | Jeremy Dodeigne : LLMs in SHS: revolutionary tools in a Wild West Territory? Reflections on costs, transparency and open science16h45 | Antoinette Rouvroy : Governing AI in Democracy17h00 | Keynote lecture on ethics and guidelines to consider when using AI in research projects and writing research articles - Bettina BERENDT, Professor at KU Leuven18h00 | Benoît Frenay and Michaël Lobet : Creation of an IA scientific committee at UNamur18:10 | DrinkA certificate of attendance, worth 0.5 cross-disciplinary doctoral training credits, will be issued on request. Contact: secretariat.adre@unamur.beThis event is free of charge, but registration is required. I want to register

JuryProf. Timoteo CARLETTI (UNamur), ChairProf. Anne-Sophie LIBERT (UNamur), secretaryProf. Benoît FRENAY (UNamur)Prof. Ugo LOCATELLI (University of Rome Tor Vergata)Prof. Konstantin BATYGIN (California Institute of Technology)Prof. Adrien LELEU (University of Geneva)AbstractThe growing number of extrasolar systems detected over the past three decades has made it necessary to develop fast and reliable methods for studying the long-term stability of planetary systems. In this work, we address the complex problem of the stability of compact three-planet systems—in which resonant and chaotic behaviors are intrinsically linked—using chaos indicators and machine learning. In the first part, we design four (variational and non-variational) chaos indicators and test their performance on a synthetic dataset. In the second part, we examine the predictive power of the chaos indicators when combined with different machine learning strategies. Finally, we apply these methods to a synthetic population generated by the Bern formation model, providing results on the long-term behavior and dynamical characterization of the multiple-planet systems in this population. Our analysis highlights the effectiveness of the dynamical tools developed here for assessing the stability of near-resonant systems and paves the way for their use in future space missions.