LSR-Net: Learning the Forward Evolution Operator for Nonlinear Fluid Dynamics
We introduce the Long-Short-Range Neural Network (LSR-Net), a novel neural operator architecture designed for data-driven forward evolution modeling, and extends it to the prediction of nonlinear fluid dynamics. LSR-Net learns the evolution operator of a dynamical system solely from pairs of initial and future state snapshots, which splits the learnable integral kernel into long-range (LR) and short-range (SR) components within stacked network blocks. While the SR component uses standard convolutions to capture local dynamics, the LR component employs a sum-of-exponentials (SOE) representation. This allows for the efficient computation of global interactions as a trainable Fourier multipl...
| Authors | Qian Hou, Sutrisno, Yuqing Li et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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