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 …
of potentially high-dimensional and nonlinear Hamiltonian functions out of datasets made of …
Forecasting causal dynamics with universal reservoirs
L Grigoryeva, J Louw, JP Ortega - arXiv preprint arXiv:2405.02536, 2024 - arxiv.org
An iterated multistep forecasting scheme based on recurrent neural networks (RNN) is
proposed for the time series generated by causal chains with infinite memory. This …
proposed for the time series generated by causal chains with infinite memory. This …
Machine learning of continuous and discrete variational ODEs with convergence guarantee and uncertainty quantification
C Offen - arXiv preprint arXiv:2404.19626, 2024 - arxiv.org
The article introduces a method to learn dynamical systems that are governed by Euler--
Lagrange equations from data. The method is based on Gaussian process regression and …
Lagrange equations from data. The method is based on Gaussian process regression and …
Data-driven identification of latent port-Hamiltonian systems
Conventional physics-based modeling techniques involve high effort, eg, time and expert
knowledge, while data-driven methods often lack interpretability, structure, and sometimes …
knowledge, while data-driven methods often lack interpretability, structure, and sometimes …
Expressiveness and Structure Preservation in Learning Port-Hamiltonian Systems
A well-specified parametrization for single-input/single-output (SISO) linear port-Hamiltonian
systems amenable to structure-preserving supervised learning is provided. The construction …
systems amenable to structure-preserving supervised learning is provided. The construction …