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Closed-form KANs integrated into IGA dramatically slash PDE solution errors by orders of magnitude, outperforming pure neural solvers and standard IGA.
PG-KINN outperforms legacy approaches by leveraging a Petrov-Galerkin framework, achieving robust solutions for complex PDEs with improved accuracy and interpretability.
Achieving up to 28% power reduction, this new multiplier redefines efficiency in modular arithmetic for RNS, crucial for high-performance computing tasks.
