Keywords

• Scope: massive data from observations, from experimental measurements, from processing, analyses or numerical simulations; Data of interest in high-energy physics, climate science, seismology, astrophysics, materials science, molecular chemistry and catalysis, engineering, and physics
• New specific models and dedicated architectures for storage, management, and computation related to massive, heterogeneous, and distributed scientific data ; switch to exascale computing
• Methods for handling imperfect, incomplete, or uncertain data ; data reconstruction and generation
• Integration of distributed, heterogeneous, multi-source, or multi-scale data: cross-referencing, aggregation, assimilation, and interoperability
• Exploration of large volumes of complex data: visualization, detection of rare events, etc.
• Dedicated methods for the exploitation and analysis of datasets: numerical simulation, statistical analysis, machine learning, artificial intelligence
• Energy and environmental impact of data processing and transfer: impact measurement, frugal methods and architectures, data reduction, multi-scale couplings
• New methods in engineering, optimization, and dynamic evolution of scientific data processing workflows
• Hybrid approaches, integrating static or dynamic models of phenomena, knowledge, and data
• Numerical simulations, hybrid simulation combining physics-informed models and machine learning