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Lagrangian_GP

Accompanying sourcecode for the article

C.Offen
Machine learning of continuous and discrete variational ODEs with guaranteed convergence and uncertainty quantifications (2024)
Status: Preprint arXiv:2404.19626

Preprint on ResearchGate, ArXiv author page, Research Information System

To reproduce the experiments of the artcile (and more), run

continuous/L_Learning_CertifiedGP_RUN.jl
discrete/Ld_Learning_CertifiedGP_RUN.jl
convergence/L_Learning_CertifiedGP_oscillator_1d_convergence_DoubleFloat.jl

Further experiments to learn discrete Lagrangians may be viewed in
discrete/Ld_Learning_CertifiedGP.ipynb

Code was run in Julia Version 1.10.2 Please refer to .toml files for package versions. The convergence test was run in a different environment. For .toml files refer to the subfolder convergence. DOI

predicted motions with uncertainty quantification predicted Hamiltonian to partially observed system with uncertainty quantification

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Accompanying sourcecode to article "Machine learning of continuous and discrete variational ODEs with guaranteed convergence and uncertainty quantifications"

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