Physics-Informed Support Vector Kernels via Green-Function Analogies and Jackson-Chebyshev Spectral Design

arXiv:2502.11153 · quant-ph, cond-mat.stat-mech, cs.AI, cs.LG, math-ph, math.MP · Submitted 2025-02-16 · Read on arXiv

quant-ph, cond-mat.stat-mech, cs.AI, cs.LG, math-ph, math.MP

Submitted: 2025-02-16

Updated: 2026-09-17

Comments: Updated version, 16 pages, 8 figures

Code: https://github.com/renatawong/svm-kernels-as-quantum-propagators

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Support Vector Machine — 11× in this paper
Alignment — 10× in this paper · explained in 1 episode
Accuracy — 7× in this paper · explained in 1 episode
International Conference on Machine Learning — 4× in this paper
Core — 3× in this paper · explained in 2 episodes
Fidelity — 3× in this paper · explained in 2 episodes
Radial Basis Function — 3× in this paper
Regularization — 3× in this paper · explained in 3 episodes
Ab Initio — 2× in this paper · explained in 1 episode
Chebyshev Polynomials — 2× in this paper · explained in 2 episodes
Quantum Physics — 2× in this paper · explained in 11 episodes
Transfer Matrix Method — 2× in this paper
Commentary — 1× in this paper · explained in 2 episodes
Feature Engineering — 1× in this paper · explained in 3 episodes
Feature Extraction — 1× in this paper · explained in 2 episodes
Hilbert Space — 1× in this paper · explained in 2 episodes
Mean Squared Error — 1× in this paper
Over-smoothing — 1× in this paper · explained in 4 episodes
Phase Transition — 1× in this paper · explained in 2 episodes
Phase Transitions — 1× in this paper · explained in 2 episodes
Physical Constraints — 1× in this paper · explained in 7 episodes
Spectral Signature — 1× in this paper · explained in 2 episodes
Support Vector Regression — 1× in this paper

Sources

Neural Operator: Graph Kernel Network for Partial Differential Equations

Related papers