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to the development of a comprehensive and robust model (data assimilation) of the Rhine Graben. The approach will be a hybrid data assimilation, physical model coupled with a neural network (PINNs). The use of soft
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boundary treatments. Integrating the surrogate models into a digital‑twin pipeline for real‑time data ingestion, assimilation, and visualisation. The project will deliver a real‑time digital‑twin
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to the Wallenberg AI, Autonomous Systems and Software Program (WASP). This enables interaction with researchers from different research fields and access to various scientific and technical expertise. Data-driven
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computational engineering and computer simulation data modeling and assimilation towards experimental measurements under consideration of uncertainties utilization of Explainable AI techniques to enable novel