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Abstract deadline:
Information:
Scientific Topics
Our goal is to provide a platform for establishing new connections between AI activities across various branches of fundamental physics, by bringing together researchers that face similar challenges and/or use similar AI solutions. The conference will be organized “horizontally”: sessions are centered on specific AI methods and themes, while being cross-disciplinary regarding the scientific questions.
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Scientific domains
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Accelerator physics
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Astroparticle physics
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Cosmology
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Experimental particle physics
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Gravitational wave physics
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Nuclear Physics
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Theoretical high-energy physics
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AI techniques and methods
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Generative models
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Pattern recognition
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Variational inference
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Optimisation techniques
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Sampling techniques
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Surrogate models
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Image analysis
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Time-series analysis
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Point-cloud analysis
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Symmetries and machine learning
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Physics-informed AI
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Diffusion models and related techniques
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Transformers and related techniques
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Foundation models
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Simulation-based/likelihood-free inference
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Likelihood-based and/or variational inference
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Integration of physics and ML
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Scientific machine learning
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Differentiable models and simulation codes
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Quantum ML
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Frugal AI
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Large Language Models
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AI for measurement/unfolding
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Other AI techniques
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AI usage
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AI for handling nuisance parameters
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Uncertainty quantification
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Explainable AI
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Trustworthy AI
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Frequentist vs Baysian inference in AI
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AI for discovery
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AI for experiment/accelerator control and operation
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AI for experimental design
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AI for simulation of physical systems
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AI for event reconstruction
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Other AI usage
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AI community
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Open software & Open data
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Benchmarks, datasets and models libraries
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ML training and platforms
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AI Ethics
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AI tools
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EuCAIF & Community building
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Community consensus finding
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AI Hardware and Acceleration
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Sustainability of ML
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Hardware acceleration
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Sharing resources
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Neuromorphic computing
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FPGAs
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AI in grid/cloud/distributed computing
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