Pages that link to "Item:Q2083739"
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The following pages link to Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739):
Displaying 11 items.
- Solving forward and inverse problems of the logarithmic nonlinear Schrödinger equation with \(\mathcal{PT}\)-symmetric harmonic potential via deep learning (Q822569) (← links)
- PINN deep learning method for the Chen-Lee-Liu equation: rogue wave on the periodic background (Q2060632) (← links)
- Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning (Q2077801) (← links)
- Prediction of the number of solitons for initial value of nonlinear Schrödinger equation based on the deep learning method (Q2107244) (← links)
- A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions (Q2135816) (← links)
- Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
- Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers \textit{via} the modified PINN (Q2169695) (← links)
- Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization (Q2684140) (← links)
- A two-component Sasa-Satsuma equation: large-time asymptotics on the line (Q6123370) (← links)
- Phase computation for the finite-genus solutions to the focusing nonlinear Schrödinger equation using convolutional neural networks (Q6177740) (← links)
- Adaptive sampling physics-informed neural network method for high-order rogue waves and parameters discovery of the \((2+1)\)-dimensional CHKP equation (Q6554449) (← links)
