Brain-inspired spiking neural networks in Engineering Mechanics: a new physics-based self-learning framework for sustainable Finite Element analysis

IF 8.7 2区 工程技术 Q1 Mathematics Engineering with Computers Pub Date : 2024-04-12 DOI:10.1007/s00366-024-01967-3
Saurabh Balkrishna Tandale, Marcus Stoffel
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Abstract

The present study aims to develop a sustainable framework employing brain-inspired neural networks for solving boundary value problems in Engineering Mechanics. Spiking neural networks, known as the third generation of artificial neural networks, are proposed for physics-based artificial intelligence. Accompanied by a new pseudo-explicit integration scheme based on spiking recurrent neural networks leading to a spike-based pseudo explicit integration scheme, the underlying differential equations are solved with a physics-informed strategy. We propose additionally a third-generation spike-based Legendre Memory Unit that handles large sequences. These third-generation networks can be implemented on the coming-of-age neuromorphic hardware resulting in less energy and memory consumption. The proposed framework, although implicit, is viewed as a pseudo-explicit scheme since it requires almost no or fewer online training steps to achieve a converged solution even for unseen loading sequences. The proposed framework is deployed in a Finite Element solver for plate structures undergoing cyclic loading and a Xylo-Av2 SynSense neuromorphic chip is used to assess its energy performance. An acceleration of more than 40% when compared to classical Finite Element Method simulations and the capability of online training is observed. We also see a reduction in energy consumption down to the thousandth order.

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工程力学中的脑启发尖峰神经网络:基于物理学的可持续有限元分析自学新框架
本研究旨在开发一个可持续的框架,利用脑启发神经网络解决工程力学中的边界值问题。尖峰神经网络被称为第三代人工神经网络,是为基于物理的人工智能而提出的。在尖峰递归神经网络的基础上,我们提出了一种新的基于尖峰的伪显式积分方案,以物理学为基础的策略求解底层微分方程。此外,我们还提出了可处理大型序列的第三代基于尖峰的 Legendre 存储单元。这些第三代网络可以在即将问世的神经形态硬件上实现,从而降低能耗和内存消耗。所提出的框架虽然是隐式的,但被视为一种伪显式方案,因为它几乎不需要或只需要较少的在线训练步骤,就能获得收敛的解决方案,即使对于未见过的加载序列也是如此。该框架被部署在一个有限元求解器中,用于对承受循环加载的板结构进行求解,并使用 Xylo-Av2 SynSense 神经形态芯片来评估其能量性能。与传统的有限元法模拟相比,该方法的速度提高了 40% 以上,并具备了在线训练的能力。我们还发现能耗降低到了千分之一。
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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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