SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications

• URL: http://arxiv.org/abs/2603.21674v1
• Date: Mon, 23 Mar 2026 08:01:43 GMT
• Title: SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
• Abstract summary: Energy efficiency remains a critical challenge in deploying physics-informed operator learning models for scientific computing.<n>We introduce the Separable Physics-informed Neuroscience-inspired Operator Network (SPINONet)<n>SPINONet incorporates regression-friendly spiking neurons through an architecture-aware design that enables sparse, event-driven computation.
• Score: 1.9573380763700712
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Energy efficiency remains a critical challenge in deploying physics-informed operator learning models for computational mechanics and scientific computing, particularly in power-constrained settings such as edge and embedded devices, where repeated operator evaluations in dense networks incur substantial computational and energy costs. To address this challenge, we introduce the Separable Physics-informed Neuroscience-inspired Operator Network (SPINONet), a neuroscience-inspired framework that reduces redundant computation across repeated evaluations while remaining compatible with physics-informed training. SPINONet incorporates regression-friendly neuroscience-inspired spiking neurons through an architecture-aware design that enables sparse, event-driven computation, improving energy efficiency while preserving the continuous, coordinate-differentiable pathways required for computing spatio-temporal derivatives. We evaluate SPINONet on a range of partial differential equations representative of computational mechanics problems, with spatial, temporal, and parametric dependencies in both time-dependent and steady-state settings, and demonstrate predictive performance comparable to conventional physics-informed operator learning approaches despite the induced sparse communication. In addition, limited data supervision in a hybrid setup is shown to improve performance in challenging regimes where purely physics-informed training may converge to spurious solutions. Finally, we provide an analytical discussion linking architectural components and design choices of SPINONet to reductions in computational load and energy consumption.

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