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This paper proposes a measurement-driven multi-layer digital twin framework for terahertz wireless data centers, using tri-band channel measurements to enable AI-based channel reconstruction and system optimization for future AI computing demands.
The paper introduces beignet, a PINN architecture that replaces random Fourier features with a trainable multi-resolution Fourier feature pyramid, achieving higher accuracy and computational efficiency on PDE benchmarks.