Description
General-purpose learning paradigms for scientific data and physics applications.
Examples include:
- Self-supervised learning
- Foundation models
- Representation learning
- Transformers
- Graph representation learning
- Generative AI and diffusion models
- Physics-informed learning
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David Rousseau (IJCLab, Université Paris-Saclay)18/11/2026 14:00AI for Physics Analysis, Inference and DiscoveryStandard talks
In collider-based particle physics experiments, proton collision events are commonly represented as tabular datasets for specific final states, features being four-vectors of final state particles or higher-level variables (invariant masses).
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Inspired by the success of foundation models in language and vision, recent developments have introduced tabular foundation models such as TabPFN and... -
Mehmet Ozgur Sahin (CEA-Saclay)18/11/2026 14:20Foundation Models, Representation Learning and Generative AIStandard talks
CaloFound uses conditional shower generation to pretrain a reusable transformer representation in an unsupervised manner for calorimeter representation and reconstruction. Using GEANT4 simulations of a CMS-like electromagnetic calorimeter, it masks a ($9\times9$) crystal window around a photon impact, randomly placing its impact crystal within the central ($3\times3$) region. The encoder...
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Humberto Reyes-Gonzalez (RWTH Aachen)18/11/2026 14:40Foundation Models, Representation Learning and Generative AIStandard talks
Jets are ubiquitous but highly complex objects at the LHC, and learning their substructure patterns provides information about the particles that produced them and, ultimately, the underlying physics. They are therefore paramount to the LHC physics program. Foundation models have emerged as a powerful new ML paradigm for modelling jets and harnessing the information encoded in their...
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Tuan Pham (IJCLab - Paris Saclay)18/11/2026 15:00Foundation Models, Representation Learning and Generative AIStandard talks
Detector simulation is among the most resource-intensive components of modern collider experiments, currently consuming roughly half of the LHC computing budget and more still in the High-Luminosity phase. Normalizing flows are attractive surrogate models for fast simulation: they sit on the Pareto frontier between inference speed and accuracy compared with competing generators such as VAEs...
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Dominique BENIELLI (Aix Marseille Université), Dr Sacha Duverger (Aix Marseille Univ, CNRS, I2M, Marseille, France)18/11/2026 15:20Foundation Models, Representation Learning and Generative AILightning talks
Representing large and complex scientific data as graphs requires methods that capture their structure across different scales while remaining computationally tractable. In this context, the FoSFor (Forest Screening for Multiscales Network Processing) toolbox, a CNRS Innovation Open project, provides a forest-based coarse-graining framework for constructing compact and multiscale graph...
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