Search Results for author: David Martín de Diego

Found 6 papers, 0 papers with code

Designing Poisson Integrators Through Machine Learning

no code implementations29 Mar 2024 Miguel Vaquero, David Martín de Diego, Jorge Cortés

This paper presents a general method to construct Poisson integrators, i. e., integrators that preserve the underlying Poisson geometry.

Completeness of Riemannian metrics: an application to the control of constrained mechanical systems

no code implementations25 Nov 2023 José Ángel Acosta, Anthony Bloch, David Martín de Diego

We introduce a mathematical technique based on modifying a given Riemannian metric and we investigate its applicability to controlling and stabilizing constrained mechanical systems.

Symmetry Preservation in Hamiltonian Systems: Simulation and Learning

no code implementations30 Aug 2023 Miguel Vaquero, Jorge Cortés, David Martín de Diego

This work presents a general geometric framework for simulating and learning the dynamics of Hamiltonian systems that are invariant under a Lie group of transformations.

Variational integrators for non-autonomous systems with applications to stabilization of multi-agent formations

no code implementations3 Feb 2022 Leonardo Colombo, Manuela Gamonal Fernández, David Martín de Diego

Numerical methods that preserve geometric invariants of the system, such as energy, momentum or the symplectic form, are called geometric integrators.

A Discrete Variational Derivation of Accelerated Methods in Optimization

no code implementations4 Jun 2021 Cédric M. Campos, Alejandro Mahillo, David Martín de Diego

Many of the new developments in machine learning are connected with gradient-based optimization methods.

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