mlsanityData to 7 Oct 2026

Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator

arXiv 2311.14710 ↗First posted 15 Nov 2023cs.NE cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

15 of 40 references.

References · 15

  1. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  2. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
  3. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  4. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  5. 2017SuperSpike: Supervised learning in multi-layer spiking neural networkscited by 178
  6. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  7. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 116
  8. 2020Event-Based Angular Velocity Regression with Spiking Networkscited by 41
  9. 2022An Analytical Estimation of Spiking Neural Networks Energy Efficiencycited by 38
  10. 2023Physics informed WNOcited by 26
  11. 2022Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?cited by 24
  12. 2022Multi-fidelity wavelet neural operator with application to uncertainty quantificationcited by 12
3 more
  1. 2023Artificial to Spiking Neural Networks Conversion for Scientific Machine Learningcited by 6
  2. 2022SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equationscited by 5
  3. 2023Waveformer for modelling dynamical systemscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operatorcited by 4