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arXiv · 2610.18875

A Boson-informed neural network for Ground States of Many-Body Schrödinger equations

Zhizhong Kong·Jerry Zhijian Yang·Cheng Yuan·Xiaofei Zhao
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Abstract

We propose a symmetry-preserving Boson-informed neural network (BINN) for the variational Monte Carlo computation of interacting-boson ground states in continuous space. The framework explicitly separates the two dominant physical scales of the system. At the microscopic interaction scale, a cell Jastrow factor absorbs the leading near-contact stiffness, while at the macroscopic scale, a potential-aware envelope encodes the external confinement, leaving the neural network to capture a smoother many-body residual. Across both two- and three-dimensional tests, the BINN approaches the Hartree and Gross-Pitaevskii limits with an empirical $\mathcal{O}(N^{-1})$ finite-size trend in both energy and density. The energies agree with diffusion Monte Carlo to within $0.06\%$ for the particle number tested up to $350$ in the three-dimensional shrinking-range case. Applications are made under various trapping scenarios, justifying robust performance of BINN.

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Zhizhong Kong, Jerry Zhijian Yang, Cheng Yuan, Xiaofei Zhao. 2026-09-05. A Boson-informed neural network for Ground States of Many-Body Schrödinger equations. https://arxiv.org/abs/2610.18875

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