[2603.21674] SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications

Physics > Computational Physics arXiv:2603.21674 (physics) [Submitted on 23 Mar 2026] Title:SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications Authors:Shailesh Garg, Luis Mandl, Somdatta Goswami, Souvik Chakraborty View a PDF of the paper titled SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications, by Shailesh Garg and Luis Mandl and Somdatta Goswami and Souvik Chakraborty View PDF HTML (experimental) 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 e...