PINNs approximate near-minimal surfaces bounding knots in S^3; their self-intersection numbers align with Fine's conjecture predictions derived from the HOMFLY polynomial.
Aspects of holographic entanglement entropy using PINNs
2 Pith papers cite this work. Polarity classification is still indexing.
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Neural networks reconstruct both spatial and timelike bulk metric components from strip entanglement entropy and Wilson loops with sub-0.2% accuracy in holographic models such as AdS-Schwarzschild and Gubser-Rocha.
citing papers explorer
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Minimal surfaces, Knots, and Neural Networks
PINNs approximate near-minimal surfaces bounding knots in S^3; their self-intersection numbers align with Fine's conjecture predictions derived from the HOMFLY polynomial.
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Holographic entanglement entropy, Wilson loops, and neural networks
Neural networks reconstruct both spatial and timelike bulk metric components from strip entanglement entropy and Wilson loops with sub-0.2% accuracy in holographic models such as AdS-Schwarzschild and Gubser-Rocha.