A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations
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Publication:2677793
DOI10.1016/j.physd.2022.133562OpenAlexW4307567559MaRDI QIDQ2677793
Shaoqun Dong, Shu-Mei Qin, Min Li, Tao Xu
Publication date: 6 January 2023
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2205.10230
deep learningparameters discovery\(N\)-coupled nonlinear equationA-WPINNvector \(N\)-soliton solutions
Uses Software
Cites Work
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- Legendre wavelets method for solving fractional partial differential equations with Dirichlet boundary conditions
- On the limited memory BFGS method for large scale optimization
- Machine learning of linear differential equations using Gaussian processes
- Hidden physics models: machine learning of nonlinear partial differential equations
- SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- N-bright-bright and N-dark-dark solitons of the coupled generalized nonlinear Schrödinger equations
- Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A proposal on machine learning via dynamical systems
- Painlevé-integrability and explicit solutions of the general two-coupled nonlinear Schrödinger system in the optical fiber communications
- Soliton collision in a general coupled nonlinear Schrödinger system via symbolic computation
- Coherently coupled bright optical solitons and their collisions
- Large Sample Properties of Simulations Using Latin Hypercube Sampling
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
- Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
- Integrable properties of the general coupled nonlinear Schrödinger equations
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