SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

2020/08/27 by Pengzhan Jin, Zhen Zhang, Aiqing Zhu +2 · 13 citations
Computer Science · Physics and Astronomy · #Model Reduction and Neural Networks #Neural Networks and Applications #Topic Modeling

paper · doi:10.1016/j.neunet.2020.08.017

openalex publication_date 2020/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Citations

• Multilayer feedforward networks are universal approximators
• Artificial neural networks for solving ordinary and partial differential equations
• ImageNet classification with deep convolutional neural networks
• Neural Ordinary Differential Equations
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Hamiltonian Neural Networks
• Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
• Unit triangular factorization of the matrix symplectic group

Cited by

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• Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
• Time-adaptive HénonNets for separable Hamiltonian systems
• Data-efficient Kernel Methods for Learning Hamiltonian Systems
• Time-adaptive SympNets for separable Hamiltonian systems
• Variational Neural Networks for Observable Thermodynamics (V-NOTS)
• FNODE: Flow-Matching for data-driven simulation of constrained multibody systems
• Symplectic convolutional neural networks
• Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
• Meta-learning Structure-Preserving Dynamics
• Moment Estimate and Variational Approach for Learning Generalized Diffusion with Non-gradient Structures
• Learning the action for long-time-step simulations of molecular dynamics