European AI for Fundamental Physics Conference 2024 (EuCAIFCon 24)

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.

• Scientific domains

  • Accelerator physics

  • Astroparticle physics

  • Cosmology

  • Experimental particle physics

  • Gravitational wave physics

  • Nuclear Physics

  • Theoretical high-energy physics

• AI techniques and methods

  • Generative models

  • Pattern recognition

  • Variational inference

  • Optimisation techniques

  • Sampling techniques

  • Surrogate models

  • Image analysis

  • Time-series analysis

  • Point-cloud analysis

  • Symmetries and machine learning

  • Physics-informed AI

  • Diffusion models and related techniques

  • Transformers and related techniques

  • Foundation models

  • Simulation-based/likelihood-free inference

  • Likelihood-based and/or variational inference

  • Integration of physics and ML

  • Scientific machine learning

  • Differentiable models and simulation codes

  • Quantum ML

  • Frugal AI

  • Large Language Models

  • AI for measurement/unfolding

  • Other AI techniques

• AI usage

  • AI for handling nuisance parameters

  • Uncertainty quantification

  • Explainable AI

  • Trustworthy AI

  • Frequentist vs Baysian inference in AI

  • AI for discovery

  • AI for experiment/accelerator control and operation

  • AI for experimental design

  • AI for simulation of physical systems

  • AI for event reconstruction

  • Other AI usage

• AI community

  • Open software & Open data

  • Benchmarks, datasets and models libraries

  • ML training and platforms

  • AI Ethics

  • AI tools

  • EuCAIF & Community building

  • Community consensus finding

• AI Hardware and Acceleration

  • Sustainability of ML

  • Hardware acceleration

  • Sharing resources

  • Neuromorphic computing

  • FPGAs

  • AI in grid/cloud/distributed computing