Pages that link to "Item:Q2167994"

The following pages link to Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994):

Displaying 22 items.

• Solving forward and inverse problems of the logarithmic nonlinear Schrödinger equation with \(\mathcal{PT}\)-symmetric harmonic potential via deep learning (Q822569) (← links)
• A deep learning improved numerical method for the simulation of rogue waves of nonlinear Schrödinger equation (Q2038155) (← 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)
• Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739) (← links)
• Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers \textit{via} the modified PINN (Q2169695) (← links)
• Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
• Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization (Q2684140) (← links)
• Data-driven forward and inverse problems for chaotic and hyperchaotic dynamic systems based on two machine learning architectures (Q2688074) (← links)
• Deep learning data-driven multi-soliton dynamics and parameters discovery for the fifth-order Kaup-Kuperschmidt equation (Q6096544) (← links)
• A new method for solving nonlinear partial differential equations based on liquid time-constant networks (Q6130983) (← links)
• Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons (Q6143642) (← links)
• Deep learning soliton dynamics and complex potentials recognition for 1D and 2D \(\mathcal{PT}\)-symmetric saturable nonlinear Schrödinger equations (Q6160033) (← links)
• Phase computation for the finite-genus solutions to the focusing nonlinear Schrödinger equation using convolutional neural networks (Q6177740) (← links)
• Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent? (Q6198233) (← links)
• Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations (Q6497269) (← links)
• \(PT\)-symmetric PINN for integrable nonlocal equations: forward and inverse problems (Q6554430) (← 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)
• A failure-informed multi-stage training algorithm for three-component nonlinear Schrödinger equation (Q6585366) (← links)
• Data-driven rogue waves solutions for the focusing and variable coefficient nonlinear Schrödinger equations via deep learning (Q6592624) (← links)
• Pseudo grid-based physics-informed convolutional-recurrent network solving the integrable nonlinear lattice equations (Q6599876) (← links)
• Novel localized wave of modified Kadomtsev-Petviashvili equation (Q6632888) (← links)