Key points are not available for this paper at this time.
The present study introduces a new physics-based self-learning spiking neural framework to compute geometrically and physically nonlinear structural response. While the so-called traditional or second-generation deep neural networks are used in many applications in the class of physics-informed neural networks, we propose a hybrid model that consists of third-generation Leaky-Integrated and Fire (LIF) neurons, Recurrent Leaky-Integrated and Fire (RLIF) neurons and second-generation dense transformation. The third-generation neurons are inspired by the human brain's energy-efficient functioning which introduces inherent temporal and sparse behavior leading to a more sustainable AI approach. However, the sparse nature of the spiking neurons poses a challenge to tackle nonlinear regression tasks. Thus, in the present study, we use an autoencoding strategy that converts the real-valued signals to its spiking representation and enables the spiking neurons to learn nonlinear material response. The proposed hybrid network is firstly pretrained with combined data-driven and physics-based loss functions and then deployed in the plastic corrector step of the implicit integration which is used in the Finite Element solver and is validated on a series of Boundary Value Problems (BVPs) consisting of plate elements. A self-learning strategy is introduced for the hybrid network to train itself during FE simulation using the proposed physics-based loss function. Two major advantages were observed: an overall computational gain in the excess of 30% and the self-learning/online training ability of the model that bolsters its convergence behavior. Finally, the proposed third-generation layers are deployed on the Xylo-Av2 neuromorphic chip and its energy performance is compared with the second-generation Recurrent Neural Networks (RNNs).
Tandale et al. (Mon,) studied this question.