Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Xupeng Cheng, Lijin Wang and Yanzhao Cao ()
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Xupeng Cheng: School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Lijin Wang: School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Yanzhao Cao: Department of Mathematics and Statistics, Auburn University, Auburn, AL 36849, USA

Mathematics, 2024, vol. 12, issue 16, 1-19

Abstract: Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error ε H arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Keywords: stochastic Hamiltonian systems; Hamiltonian Neural Networks; Gaussian numerical quadrature; Simpson’s formula (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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