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Probabilistic Physics-Informed Neural Solvers for Woods-Saxon Parameter Identification: A Coupled Forward-Inverse Approach
The Woods-Saxon mean-field approach offers a compact description of bound single-particle motion in finite nuclei, while physics-informed neural networks (PINNs) provide a differentiable route to the inverse problem of recovering potential parameters from sparse spectral data. We develop a probabili
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