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A Structure-Preserving Kernel Method for Learning Hamiltonian Systems
A structure-preserving kernel ridge regression method is presented that allows the recovery of potentially high-dimensional and nonlinear Hamiltonian functions out of datasets made of noisy observations of Hamiltonian vector fields.
An Onsager-Machlup approach to the most probable transition pathway for a genetic regulatory network
We investigate a quantitative network of gene expression dynamics describing the competence development in Bacillus subtilis.
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