Low-Latency Activation-Regularized Sparse Neural Operators with Distillation Assistance Towards Real-Time Neuromorphic Virtual Sensing

arXiv:2608.23987 · cs.LG · Submitted 2026-08-25 · Read on arXiv

cs.LG

Submitted: 2026-08-25

Updated: 2026-09-28

Terminology

• Sparsity — 110× in this paper · explained in 4 episodes
• Regularization — 63× in this paper · explained in 3 episodes
• Accuracy — 51× in this paper · explained in 1 episode
• Distillation — 33× in this paper · explained in 2 episodes
• Aggregation — 25× in this paper · explained in 1 episode
• Reconstruction Error — 23× in this paper · explained in 2 episodes
• Benchmark — 22× in this paper · explained in 2 episodes
• Activation Sparsity — 21× in this paper · explained in 1 episode
• Activation Function — 15× in this paper · explained in 1 episode
• Knowledge Distillation — 12× in this paper · explained in 11 episodes
• Variable Spiking Neuron — 12× in this paper
• Lid-Driven Cavity — 9× in this paper
• Energy Efficiency — 8× in this paper · explained in 3 episodes
• Fidelity — 7× in this paper · explained in 2 episodes
• Synthetic Data Generation — 5× in this paper · explained in 5 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Edge Deployment — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Finite Element Method — 3× in this paper
• Loss Function — 3× in this paper · explained in 2 episodes
• National Center for Supercomputing Applications — 3× in this paper
• Pruning — 3× in this paper · explained in 4 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• DeepONet — 2× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Fourier Neural Operator — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Leaky Integrate-and-Fire — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Physics-Informed Neural Networks (PINNs) — 1× in this paper · explained in 6 episodes
• Predictive Maintenance — 1× in this paper · explained in 2 episodes
• Spiking Neural Networks (SNNs) — 1× in this paper · explained in 3 episodes

Sources

• Fourier Neural Operator for Parametric Partial Differential Equations

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