A non-overlapping Schwarz hybrid FE-NO framework with Point-DeepONet enables efficient, geometry-flexible simulations of solid mechanics by reducing interface iterations and enforcing mechanical consistency through analytical strain-stress derivation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
SCNO composes pre-trained spiking neural operator blocks for elementary PDE terms to solve unseen coupled PDEs with a frozen library plus a lightweight correction network, achieving lower error than monolithic baselines using only 95K parameters.
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SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
SCNO composes pre-trained spiking neural operator blocks for elementary PDE terms to solve unseen coupled PDEs with a frozen library plus a lightweight correction network, achieving lower error than monolithic baselines using only 95K parameters.