73 citations · 670 across the 56 of their papers we have counts for
5 papers · 1 filter
Learning Hamiltonians from Trajectory Data with Kernel Methods
Jianyu Hu, David Martin de Diego, Juan-Pablo Ortega +1
We study the recovery of a Hamiltonian from discrete trajectory observations using structure-preserving kernel methods. When exact-flow data are fitted through a symplectic one-ste…
A Unified Discrete Gradient-SAV Framework for Structure-Preserving Integration
Elena Celledoni, David Martín de Diego, Brynjulf Owren +1
We present a framework combining discrete gradient (DG) methods with the Scalar Auxiliary Variable (SAV) approach to construct structure-preserving integrators for dissipative and…
Discrete-time generalized canonical transformations for non-autonomous systems
Leonardo Colombo, David Martin de Diego, Riccardo Muradore +2
A dynamical system is said to be \emph{non-autonomous} when the differential equations describing its evolution depends explicitly on time. Among the various geometric approaches t…
Learning Forced Multibody Dynamics on Lie Groups
Martine Dyring Hansen, Marta Ghirardelli, Elena Celledoni +2
We propose an architecture for learning the dynamics of mechanical systems based on discrete forced Euler-Lagrange equations on Lie groups using only position data. By formulating…
Variational Inverse Problems for Second Order ODEs with and without Constraints
G. E. Prince, T. Mestdag, D. Martin de Diego
Many physical systems with or without nonholonomic constraints have a Lagrangian description. In the first case, the Lagrangian model can be represented by second-order ODEs that a…