Fractional Laplace Neural Operators: Exact Architectures, an Expressivity Frontier at Criticality, and Certified Stability for Memory-Driven Network Dynamics

arXiv:2610.00515 · stat.ML, cs.LG · Submitted 2026-09-30 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/pcm-dpc/COVID-19

Terminology

• Benchmark — 18× in this paper · explained in 2 episodes
• DeepONet — 12× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Consistency — 6× in this paper · explained in 3 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• Bifurcation Analysis — 2× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Homogenization — 2× in this paper · explained in 2 episodes
• Provenance — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Chebyshev Polynomials — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 1× in this paper
• Markovian Approximation — 1× in this paper · explained in 2 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Universality Class — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

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