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velocity anomalies (Vp, Vp/Vs). These observations will be quantitatively compared with model predictions to test whether the system is dominated by the geometric interactions of the faults, the rheological
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-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical
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analysis of the ATLAS experiment data. The L2IT team plays a driving role within the ATLAS collaboration for the reconstruction of charged particle tracks using geometric deep learning (GDL). The person who
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advancements achieved with the LHC is the discovery of the Higgs boson by the ATLAS and CMS collaborations in 2012, providing crucial experimental verification of the Standard Model (SM) of particle physics
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–Science research-creation project bringing together engineering, life sciences and design. This interdisciplinary framework will enable the candidate to develop innovative experimental models while engaging
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width: discrete frequency comb ver-sus continuous spectrum, and the presence or absence of spectral dispersion. Added to this are kinetic effects (electron trapping, plasma wave broadening) and geometric
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computationally. Second, the research will focus on developing a controlled generative AI system capable of integrating these ontological and geometric constraints. Diffusion models will be adapted through
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naturally arises when a graph is used to model a temporal or dynamic process. Furthermore, many graph classes are defined as intersection graphs of geometric objects; in such cases, the coordinates