Complex dynamics on the one-dimensional quantum droplets via time piecewise PINNs
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Publication:6096531
DOI10.1016/j.physd.2023.133851arXiv2305.01553OpenAlexW4384283676MaRDI QIDQ6096531
Publication date: 12 September 2023
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2305.01553
Related Items (3)
Deep neural networks learning forward and inverse problems of two-dimensional nonlinear wave equations with rational solitons ⋮ 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?
Cites Work
- Unnamed Item
- PINN deep learning method for the Chen-Lee-Liu equation: rogue wave on the periodic background
- Dynamics of 1D and 3D quantum droplets in parity-time-symmetric harmonic-Gaussian potentials with two competing nonlinearities
- Breather excitations on the one-dimensional quantum droplet
- A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions
- \(N\)-double poles solutions for nonlocal Hirota equation with nonzero boundary conditions using Riemann-Hilbert method and PINN algorithm
- Physics-informed neural networks for high-speed flows
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization
- Bose-Einstein Condensation and Superfluidity
- Eigenvalues and Eigenfunctions of a Bose System of Hard Spheres and Its Low-Temperature Properties
- Structure of a quantized vortex in boson systems
- Large Sample Properties of Simulations Using Latin Hypercube Sampling
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
- fPINNs: Fractional Physics-Informed Neural Networks
- Learning representations by back-propagating errors
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