Work

Physics-Informed Neural Networks for Nonlinear Dispersive Equations

Year: 2026

Type: software

Abstract: Initial reproducibility release for the manuscript: Physics-Informed Neural Networks for Nonlinear Dispersive Equations This release contains the PyTorch implementation used to reproduce the numerical experiments, ablation studies, seed-robustness tables, and Kawahara spectral-error diagnostics reported in the manuscript. Included components: Standard physical-space PINN scripts for KdV, BBM, Kawahara, and Rosenau--KdV. Kawahara Fourier error-spectrum diagnostic. Kawahara phase-shift/spectral-control diagnostic. MIT License and citation metadata.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.21001915

Author: Koshkarbayev, Nurbol

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7166473809

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