Learning Effective Soliton Dynamics from Scattering Data
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Abstract (arXiv)
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.
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References · 6
- 2020Weak SINDy For Partial Differential Equationscited by 154
- 2024Weak-Form Inference for Hybrid Dynamical Systems in Ecologycited by 7
- 2021Combining machine learning with physics: A framework for tracking and sorting multiple dark solitonscited by 4
- 2025Computer Assisted Discovery of Integrability via SILO: Sparse Identification of Lax Operatorscited by 2
- 2026Learning effective models from network dynamics data with multiple initial conditions using weak form SINDycited by 2
- 2025Robust Moment Identification for Nonlinear PDEs via a Neural ODE Approachcited by 1
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