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Found 4 papers, 3 papers with code
Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning
Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations.
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems
In this paper, we introduce a new structure-preserving deep learning architecture, the Lie group Forced Variational Integrator Network (LieFVIN), capable of learning controlled Lagrangian or Hamiltonian dynamics on Lie groups, either from position-velocity or position-only data.
General Techniques for Constructing Variational Integrators
These two characterizations lead to the Galerkin and shooting-based constructions for discrete Lagrangians, which depend on a choice of a numerical quadrature formula, together with either a finite-dimensional function space or a one-step method.
Numerical Analysis
Control of Complex Maneuvers for a Quadrotor UAV using Geometric Methods on SE(3)
The flight maneuvers are defined by a concatenation of flight modes or primitives, each of which is achieved by a nonlinear controller that solves an output tracking problem.
Optimization and Control Systems and Control
