Pages that link to "Item:Q2679950"

The following pages link to Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator (Q2679950):

Displaying 7 items.

• Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
• Data-driven forward and inverse problems for chaotic and hyperchaotic dynamic systems based on two machine learning architectures (Q2688074) (← links)
• Inverse scattering transform for the integrable fractional derivative nonlinear Schrödinger equation (Q6118138) (← links)
• Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985) (← links)
• The Riemann-Hilbert approach for the integrable fractional Fokas-Lenells equation (Q6575463) (← links)
• A failure-informed multi-stage training algorithm for three-component nonlinear Schrödinger equation (Q6585366) (← links)
• On examining the predictive capabilities of two variants of the PINN in validating localized wave solutions in the generalized nonlinear Schrödinger equation (Q6649759) (← links)