in numerical simulation
SL-DRF-21-0147 - Simulation-assisted machine learning for infrared measurement in tokamak.
SL-DRF-21-0279 - Advanced and artificial intelligence techniques to mitigate linear and non-linear imperfections in future circular colliders.
SL-DRF-21-0283 - Study and modeling of an axisymmetric electron cyclotron resonance ion source.
SL-DRF-21-0284 - Systematic studies of the continuum-coupling correlations in near-threshold states.
SL-DRF-21-0285 - Study of reaction mechanisms for the synthesis of super-heavy elements.
SL-DRF-21-0293 - Pushing ab initio calculations of atomic nuclei to higher precision.
SL-DRF-21-0297 - Continuum QCD approaches and 3D structure of the nucleon.
SL-DRF-21-0316 - Deep Learning and gamma spectroscopy: a new signal processing approach for CdTe detectors data analysis..
SL-DRF-21-0332 - Cosmology - Clusters of galaxies - Artificial intelligence.
SL-DRF-21-0336 - Uncertainties for large scale deep learning-based image reconstruction.
SL-DRF-21-0349 - Design of a novel analog to digital converter with internal machine learning calibration.
SL-DRF-21-0460 - Development and benchmarking of novel AMR-PIC methods for the realistic 3D modelling of light-matter and light-vacuum interactions at extreme intensities.
SL-DRF-21-0478 - Artificial intelligence to simulate big data and search for the Higgs boson decay to a pair of muons with the Atlas experiment at the Large Hadron Collider.
SL-DRF-21-0556 - Turbulence - neutrals interaction and its impact on density regimes in the edge plasma of tokamaks.
SL-DRF-21-0568 - Gluon tomography with exclusive vector meson production.
SL-DRF-21-0636 - Reconstruction of coherent non monotonic density profiles in tokamaks using IA algorithms including several measurent sources.
SL-DRF-21-0660 - Simulating edge plasma turbulence for ITER: Improving the numerical resolution of a very anisotropic, poorly conditioned diffusion problem.
SL-DRF-21-0669 - Artificial intelligence and fusion plasma control: Application to the WEST tokamak.
SL-DRF-21-0672 - Control of runaway electrons by RF waves.
SL-DRF-21-0755 - Deep learning to discover rare complex signals with the Atlas experiment at the LHC.
CEA is a French government-funded technological research organisation in four main areas: low-carbon energies, defense and security, information technologies and health technologies. A prominent player in the European Research Area, it is involved in setting up collaborative projects with many partners around the world.