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Partial differntiAl equatioN founDation models and their Application to mobile networks

CORDIS · observation · Publication date unknown

y, PDEs are solved through numerical methods, which are invariably computationally intensive, thus curbing the adoption of these techniques in intricate problems and real-time applications. Recently, artificial intelligence (AI)-driven approaches have emerged as promising alternatives to approximate with remarkable speed and accuracy, the solution of physics-based PDEs, and ultimately supplant legacy numerical PDE solvers. This action will explore the development of AI-powered frameworks for the resolution of a wide range of physics-based PDEs. These frameworks will be underpinned by universal neural operators that can capture multi-scale and non-linear interactions present in physical phenomena, thereby enabling accurate predictions of physical system behaviour even under dynamic conditions and different families of PDEs. Building on this foundation, the AI-based PDE solvers will be fine-tuned and leveraged to emulate the dynamics of technological non-physical systems. Specifically, they will be employed to forecast the spatiotemporal mobile network traffic demands and user mobili

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recordType

award

status

SIGNED

region

EU

value

365148.6

unit

EUR