Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions
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Publication:2683577
DOI10.1016/j.physd.2022.133629OpenAlexW4313529129MaRDI QIDQ2683577
Publication date: 14 February 2023
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2211.15526
Related Items (9)
VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient ⋮ Deep learning data-driven multi-soliton dynamics and parameters discovery for the fifth-order Kaup-Kuperschmidt equation ⋮ Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems ⋮ Parallel physics-informed neural networks method with regularization strategies for the forward-inverse problems of the variable coefficient modified KdV equation ⋮ Physics-informed neural networks with two weighted loss function methods for interactions of two-dimensional oceanic internal solitary waves ⋮ Deep learning soliton dynamics and complex potentials recognition for 1D and 2D \(\mathcal{PT}\)-symmetric saturable nonlinear Schrödinger equations ⋮ Exploring two-dimensional internal waves: a new three-coupled Davey-Stewartson system and physics-informed neural networks with weight assignment methods ⋮ Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations ⋮ Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent?
Uses Software
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