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Session Chair: Daniel Brunner, FEMTO-ST, CNRS, France
Location:Mercurey
Presentations
3:30pm - 4:00pm Invited ID: 418 / FS4 S01: 1 Focused Sessions 4: Machine-Learning for Optics and Photonic Computing for AI
Towards an Artificial Muse for new ideas in Physics
Mario Krenn
Max Planck Instititute for the Science of Light, Germany
Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding or inspire new surprising ideas. I will talk about how AI can be used as an artificial muse in quantum physics, which suggests surprising and unconventional ideas and techniques that the human scientist can interpret, understand and generalize to its fullest potential.
[1] Krenn, Kottmann, Tischler, Aspuru-Guzik, Conceptual understanding through efficient automated design of quantum optical experiments. Physical Review X 11(3), 031044 (2021).
[3] Krenn, Zeilinger, Predicting research trends with semantic and neural networks with an application in quantum physics. PNAS 117(4), 1910-1916 (2020).
4:00pm - 4:15pm ID: 252 / FS4 S01: 2 Focused Sessions 4: Machine-Learning for Optics and Photonic Computing for AI
Analysing interaction and localization dynamics in modulation instability via data-driven dominant balance
Andrei V. Ermolaev1, Mehdi Mabed1, Christophe Finot2, Goëry Genty3, John M. Dudley1
1Université de Franche-Comté, Institut FEMTO-ST, CNRS UMR 6174, Besançon, France; 2Université de Bourgogne, Laboratoire Interdisciplinaire Carnot de Bourgogne, CNRS UMR 6303, Dijon, France; 3Photonics Laboratory, Tampere University, Tampere, FI-33104, Finland
We report the first application of the Machine Learning technique of data-driven dominant balance to optical fiber noise-driven Modulation Instability, with the aim to automatically identify local regions of dispersive and nonlinear interactions governing the dynamics. We first consider the analytical solutions of Nonlinear Schrödinger Equation – solitons on finite background – where it is shown that dominant balance distinguishes two particularly different dynamical regimes: one where the nonlinear process is dominating the dispersive propagation, and one where nonlinearity and second order dispersion act together driving the localization of breathers. By means of numerical simulations, we then analyse the spatio-temporal dynamics of noise-driven Modulation Instability and demonstrate that data-driven dominant balance can successfully identify the associated dominating physical regimes even within the turbulent dynamics.
4:15pm - 4:30pm ID: 491 / FS4 S01: 3 Focused Sessions 4: Machine-Learning for Optics and Photonic Computing for AI
Long-term forecasting of extreme events such as oceanic rogue waves, heat waves, floods, earthquakes, has always been a challenge due to their highly complex dynamics. Recently, machine learning methods have been used for model-free forecasting of physical systems. In this work, we investigated the ability of these methods to forecast the emergence of extreme events in a spatiotemporal chaotic passive ring cavity by detecting the precursors of high intensity pulses. To this end, we have implemented supervised sequence (precursors) to sequence (pulses) machine learning algorithms, corresponding to a local forecasting of when and where extreme events will appear.
