High-fidelity three-dimensional turbulent flow fields are essential for scientific and engineering analyses, yet obtaining them through direct numerical simulation or high-resolution measurements remains expensive. Super-resolution reconstruction from coarse-grained data therefore offers a promising alternative, but re...
Han-Lin Kong, Jia-Nan Wei, Yun-Tian Chen et al.· 0 citations
Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth System Models (ESMs) remains limited by coarse spatial resolution and systematic biases. To address this, data-driven down scaling techniques have been widely used to en...
Chen Yu, Jia-Nan Wei, Han-Lin Kong et al.· 0 citations
Frozen evaluation reduces the per-geometry cost by $986\times$ relative to independent optimization, yielding an estimated $25.8\times$ end-to-end GPU-cost reduction on a 161-point N$_2$ grid.
Li-Zhong Fu, Jia-Nan Wei, Wen-Guan Wang et al.· 0 citations
SBFNav is introduced, a closed-loop navigation framework centered on a language- conditioned Spatial Belief Field (SBF) that preserves multiple spatial hypotheses under par- tial evidence and further confirms the advantages of spatial-belief modeling over single-point prediction.
Hao-Tian Xu, Yue Hu, Zheng-Qiu Zhu et al.· 0 citations
Together, these results place autoregressive neural quantum states within the broader landscape of empirical neural scaling and open a quantitative route toward the systematic scaling of neural quantum solvers for ab initio quantum chemistry.
Chen Yu, Han-Lin Kong, Jia-Nan Wei et al.· 0 citations
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