Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator
This addresses the problem of practical AI deployment in mechanics for edge computing by reducing energy needs, though it appears incremental as it builds on existing neural operator and spiking network methods.
The paper tackles the high energy and resource requirements of AI algorithms in mechanics by proposing a Variable Spiking Wavelet Neural Operator (VS-WNO) that uses variable spiking neurons to enable sparse communication, allowing convergence to ground truth in regression tasks like solving partial differential equations.
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics applications. With recent developments like the introduction of neural operators, AI's potential for being used in mechanics applications has increased significantly. However, AI's immense energy and resource requirements are a hurdle in its practical field use case. The proposed VS-WNO is based on the principles of spiking neural networks, which have shown promise in reducing the energy requirements of the neural networks. This makes possible the use of such algorithms in edge computing. The proposed VS-WNO utilizes variable spiking neurons, which promote sparse communication, thus conserving energy, and its use is further supported by its ability to tackle regression tasks, often faced in the field of mechanics. Various examples dealing with partial differential equations, like Burger's equation, Allen Cahn's equation, and Darcy's equation, have been shown. Comparisons have been shown against wavelet neural operator utilizing leaky integrate and fire neurons (direct and encoded inputs) and vanilla wavelet neural operator utilizing artificial neurons. The results produced illustrate the ability of the proposed VS-WNO to converge to ground truth while promoting sparse communication.