Neuroscience Inspired Graph Operators Towards Edge-Deployable Virtual Sensing for Irregular Geometries

• URL: http://arxiv.org/abs/2604.16722v1
• Date: Fri, 17 Apr 2026 21:53:20 GMT
• Title: Neuroscience Inspired Graph Operators Towards Edge-Deployable Virtual Sensing for Irregular Geometries
• Abstract summary: Spiking neuron integration can provide a potential solution when integrated on neuromorphic hardware.<n>Current existing neuron models result in severe performance degradation towards regression-based virtual sensing.<n>We present the Variable Spiking Graph Neural Operator (VS-GNO) which integrates a sophisticated spectral-error convolutional analysis and energy-error balance loss function.
• Score: 4.190153363488523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting full-field physics through the real-time virtual sensing of engineering systems can enhance limited physical sensors but often requires sparse-to-dense reconstruction, complex multiphysics, and highly irregular geometries as well as strict latency and energy constraints for edge-deployability. Neural operators have been presented as a potential candidate for such applications but few architectures exist that explicitly address power consumption. Spiking neuron integration can provide a potential solution when integrated on neuromorphic hardware but the current existing neuron models result in severe performance degradation towards regression-based virtual sensing. To address the performance concerns and edge-constraints, we present the Variable Spiking Graph Neural Operator (VS-GNO) which integrates a sophisticated spectral-spatial convolutional analysis and a previously developed Variable Spiking Neuron (VSN) and energy-error balance loss function. With a non-spiking $L_2$ error baseline of $0.4\%$, VS-GNO can provide a reconstruction error of $0.71\%$ with $15\%$ average spiking in its spectral-only form and $1.04\%$ with $24.5\%$ spiking in its entire form. These results position VS-GNO as a promising step towards energy-efficient, edge-deployable neural operators for real-time sparse-to-dense virtual sensing in complex, highly irregular engineering environments.

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