We introduce a sustainable neuromorphic approach for numerical simulations in Engineering Mechanics. The finite element method (FEM) is widely used in engineering design; however, worldwide, there is no neuromorphic technology available in mechanics, even though the need for computational capacity with complex mechanical models is increasing1. AI-enhanced engineering approaches, such as agent-based models2, intensify the high energy demand coupled to CO2 emissions3,4. However, the solution of mechanical boundary value problems in numerical simulations on neuromorphic chips accounting for physical and geometrical nonlinearities has not been researched. Here we show that complex mechanical phenomena can be approximated by spiking neural networks (SNN), thereby creating a new sparse signal transmission of mechanical state variables. We found that hybrid neural networks, composed of sparse and non-sparse neuronal activity, result in the best trade-off between accuracy and energy savings. The required energy is reduced by several orders of magnitude compared to classical numerical simulations, which can significantly lower CO2 emissions in time-consuming computations. Due to their inherent path dependency, spike activation models are well-suited for nonlinear regression and physics-based approaches in nonlinear mechanics. Our results demonstrate how neuromorphic computing can be applied to a wide range of structural forming processes in engineering, since it differs fundamentally from neuromorphic classification studies in the literature and provides the basis for function approximation. Moreover, it is investigated how neural networks and non-machine learning operations can be deployed on Field-Programmable Gate Arrays (FPGAs). We anticipate our study to be a starting point for more sustainable AI models in engineering science and related disciplines.
