Exterior complex scaling enables physics-informed neural networks for quantum scattering

Abstract:Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving differential equations, yet their application to nuclear scattering has been hindered by the oscillatory, non-decaying nature of scattering wave functions. In this work, I demonstrate that exterior complex scaling (ECS) transforms scattering boundary conditions into exponentially decaying waves suitable for neural network solutions, enabling PINNs to solve nuclear reaction problems for the first time. I develop a driven-equation formulation where the source term is confined to the real axis, avoiding the need to analytically continue nuclear potentials into the complex plane. The method is validated on nucleon-nucleus scattering (n+$^{40}$Ca at $E_{\text{lab}}=20$~MeV) with 21 partial waves, achieving phase shift accuracy of $\Delta\delta \lesssim 0.1^\circ$ for the strongly absorbed channels ($\ell \leq 4$) and $\Delta\delta \leq 0.60^\circ$ for all channels up to $\ell = 10$, when compared to conventional solvers. I further demonstrate the approach on heavy-ion scattering ($^6$Li+$^{208}$Pb at 40~MeV) with 41 partial waves and strong Coulomb effects, where an auto-adaptive anchor warm-down for weak-source channels yields a mean S-matrix accuracy of $|\Delta|S_\ell|| \approx 3 \times 10^{-3}$ across the full angular momentum range, including the absorption-to-transparency transition region. This work establishes the foundation for extending PINNs to inverse problems where end-to-end differentiability enables direct fitting of optical potential parameters, coupled-channel reactions, and few-body scattering where traditional grid methods face exponential scaling.

Nuclear Theory (nucl-th); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)

arXiv:2602.04553 [nucl-th]

(or arXiv:2602.04553v2 [nucl-th] for this version)

https://doi.org/10.48550/arXiv.2602.04553 Focus to learn more arXiv-issued DOI via DataCite

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