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Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, India
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Uniform sampling fails to adequately cover the vulnerability landscape of neural operators, as optimal training data is architecture-dependent, necessitating targeted data generation strategies.
Forget retraining monolithic models: SCNO unlocks zero-forgetting modular expansion for PDE solvers by composing spiking neural operator blocks, outperforming traditional methods with fewer parameters.
Achieve real-time, accurate virtual sensing on edge devices with VIRSO, a graph neural operator that slashes energy consumption by 20x compared to previous methods.
