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.
Spiking neural operators for scientific machine learning
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2026 2verdicts
UNVERDICTED 2representative citing papers
A language model-based operator learning method reconstructs flow fields from under 10% sparse measurements on vortex street, US temperature, blood flow, and turbulent jet benchmarks with competitive accuracy.
citing papers explorer
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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.
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Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
A language model-based operator learning method reconstructs flow fields from under 10% sparse measurements on vortex street, US temperature, blood flow, and turbulent jet benchmarks with competitive accuracy.