Pseudo grid-based physics-informed convolutional-recurrent network solving the integrable nonlinear lattice equations

2024/07/29 by Zhe Lin, Yong Chen · 1 citation
Engineering · Physics and Astronomy · #Fluid Dynamics and Turbulent Flows #Lattice Boltzmann Simulation Studies #Model Reduction and Neural Networks

paper · doi:10.1016/j.physd.2024.134304

openalex publication_date 2024/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Citations

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Long Short-Term Memory
• Multilayer feedforward networks are universal approximators
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
• NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
• Fourier Neural Operator for Parametric Partial Differential Equations
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Physics-informed neural networks method in high-dimensional integrable systems
• Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
• Uncovering solutions from data corrupted by systematic errors: A physics-constrained convolutional neural network approach
• VC-PINN: Variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient

Cited by

• Physics-informed neural networks for high-dimensional solutions and snaking bifurcations in nonlinear lattices