Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization
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Publication:2684140
DOI10.1016/j.cnsns.2022.107051OpenAlexW4312084884MaRDI QIDQ2684140
Publication date: 16 February 2023
Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2112.04062
Artificial neural networks and deep learning (68T07) Partial differential equations of mathematical physics and other areas of application (35Qxx) Incompressible inviscid fluids (76Bxx)
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Complex dynamics on the one-dimensional quantum droplets via time piecewise PINNs ⋮ Physics-informed ConvNet: learning physical field from a shallow neural network ⋮ Lump wave solutions, lump-stripe soliton inelastic collision phenomena and rogue-type wave solutions for a generalized breaking soliton system in \((3+1)\)-dimensions
Uses Software
Cites Work
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