Skip to results
MLSift
← Feed
AI for Science & EngineeringInverse Scattering Transform2607.01545

Learning Effective Soliton Dynamics from Scattering Data

Seth Minor, Vanja Dukic, David M. Bortz

physics.comp-ph nlin.SI stat.ML

Abstract

The inverse scattering transform (IST) provides the standard theoretical framework for deriving soliton dynamics. Traditionally, such derivations have been of an analytical, rather than data-driven, nature. In this paper, we combine the conceptual framework of the IST with weak-form system identification methods to discover effective soliton dynamics directly from observed scattering data, without assuming prior knowledge of the scattering equations. Our method avoids parameterizing solitary waves via ad hoc curve-fitting by working in the scattering domain, yielding interpretable low-dimensional models that remain valid in perturbed and near-integrable regimes. We demonstrate the performance of the proposed approach on synthetic and experimental data governed by shallow-water equations of Korteweg--de Vries-type and recover models that are consistent with canonical IST theory.

Topics

Classified with taxonomy v2 on Sat, 5 Sept 2026.

The PDF is 1–3 MB. Open it in your browser's viewer, or load it here.

Open PDF