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

TL;DR

SPINONet is a neuroscience-inspired neural operator that enhances energy efficiency and reduces computation in physics-informed models for computational mechanics, suitable for power-constrained environments.

Contribution

The paper introduces SPINONet, a novel spiking neural network architecture that integrates physics-informed training with sparse, event-driven computation for scalable mechanics applications.

Findings

SPINONet achieves comparable accuracy to traditional methods.

It significantly reduces computational load and energy consumption.

Hybrid training improves performance in challenging regimes.

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.