The data-driven localized wave solutions of the derivative nonlinear Schrödinger equation by using improved PINN approach
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Publication:2124077
DOI10.1016/j.wavemoti.2021.102823OpenAlexW3197053878MaRDI QIDQ2124077
JunCai Pu, Yong Chen, Wei-Qi Peng
Publication date: 14 April 2022
Published in: Wave Motion (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2106.14493
derivative nonlinear Schrödinger equationdata-driven localized wave solutionsimproved physics-informed neural networks
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Cites Work
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- On the limited memory BFGS method for large scale optimization
- On the derivative nonlinear Schrödinger equation
- Rogue waves in the generalized derivative nonlinear Schrödinger equations
- The derivative nonlinear Schrödinger equation with zero/nonzero boundary conditions: inverse scattering transforms and \(N\)-double-pole solutions
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning
- Rogue waves generation through multiphase solutions degeneration for the derivative nonlinear Schrödinger equation
- The hierarchy of higher order solutions of the derivative nonlinear Schrödinger equation
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Modified Nonlinear Schrödinger Equation for Alfvén Waves Propagating along the Magnetic Field in Cold Plasmas
- Multi-Soliton Solutions of a Derivative Nonlinear Schrödinger Equation
- Human-level concept learning through probabilistic program induction
- The Darboux transformation of the derivative nonlinear Schrödinger equation
- Alfven solitons
- Large Sample Properties of Simulations Using Latin Hypercube Sampling
- An exact solution for a derivative nonlinear Schrödinger equation
- The hierarchy of multi-soliton solutions of the derivative nonlinear Schr dinger equation
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- Learning representations by back-propagating errors
- On improving the effectiveness of periodic solutions of the NLS and DNLS equations
- Solving second-order nonlinear evolution partial differential equations using deep learning
- A deep learning method for solving third-order nonlinear evolution equations
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