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Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation laws

Jingdong Zhang ^1,2, Qunxi Zhu ^2,3,4,*, and Wei Lin ^1,2,3,4,†

¹School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China
²Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
³Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
⁴MOE Frontiers Center for Brain Science and State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai 200032, China

^*qxzhu16@fudan.edu.cn
^†wlin@fudan.edu.cn

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Articles Citing

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation laws

Herglotz Lagrangian neural networks: Learning dissipative dynamics via contact variational principles

Dynamics creation through neural dynamical transfer learning

Exact solitary waves in pre-compressed granular media

Constants of motion network reexamined

Integrated Utilization of Equations and Small Dataset in the Koopman Operator: Applications to Forward and Inverse Problems

MacHa: Multi-Aspect Controllable Text Generation Based on a Hamiltonian System

Neural network-based symbolic calculation approach for solving the Korteweg–de Vries equation

Lump, breathing inelastic collision phenomena and rogue wave solutions for a extended KP hierarchy-type equation by neural network-based method

Compression of the Koopman matrix for nonlinear physical models via hierarchical clustering

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Neural Koopman Prior for Data Assimilation

Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction

Learning the dynamics of multispectral satellite image time series

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