Symplectic neural networks in Taylor series form for Hamiltonian systems

DOI10.1016/j.jcp.2021.110325OpenAlexW3022424628WikidataQ114163461 ScholiaQ114163461MaRDI QIDQ2124341

Guanghan Pan, Xingzhe He, Yunjin Tong, Shiying Xiong, Bo Zhu

Publication date: 8 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.04986

zbMATH Keywords

Hamiltonian systemmachine learningTaylor series expansionphysics-informed neural network

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Partial differential equations of mathematical physics and other areas of application (35Qxx) Hamiltonian and Lagrangian mechanics (70Hxx)

Related Items (8)

Tuning Symplectic Integrators is Easy and Worthwhile ⋮ VPNets: volume-preserving neural networks for learning source-free dynamics ⋮ Scientific machine learning through physics-informed neural networks: where we are and what's next ⋮ A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty ⋮ Efficient Bayesian inference with latent Hamiltonian neural networks in no-U-turn sampling ⋮ Symplectic learning for Hamiltonian neural networks ⋮ Locally-symplectic neural networks for learning volume-preserving dynamics ⋮ NySALT: Nyström-type inference-based schemes adaptive to large time-stepping

Uses Software

• Adam
• torchdiffeq
• DGM

Cites Work

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