Author

Han-Lin Kong

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#artificial intelligence Preprint Oct 2026

High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics

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

#machine learning Preprint Sep 2026

Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling

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

Preprint Aug 2026

Scaling Neural Network Quantum States for Ab Initio Quantum Chemistry

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