SOURCE-LINKED INTELLIGENCE

Data-driven Simulations for Understanding Reconnection and GEomagnetism

CORDIS · observation · Publication date unknown

r our space born assets such as satellites and space missions. The goal is to build a global physics-based magneto-fluid model of plasma with AI component that represents such microscopic physics via machine learning algorithms trained on particle in cell (PIC) simulations. By introducing physics-informed data-driven models, we aim to bridge the gap between local and global scales, offering unprecedented accuracy in simulating space weather phenomena. Such data-driven models will allow us to better understand and predict the evolution of magnetic storms crucial for safeguarding modern technological infrastructure. What makes this goal now possible in this interdisciplinary project are the advances in scientific machine learning in meteorology which the PI plans to bringing from environmental data science to plasma physics as well as the PI's current involvement in energy-conserving Particle in Cell simulations and previous experience in kinetic and magneto-fluid modelling. These innovations will lead to physics-informed data driven model that will be validated with space mission obs

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR