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Found 6 papers, 1 papers with code
Optimal Potential Shaping on SE(3) via Neural ODEs on Lie Groups
The optimal control problem is phrased as an optimization of a neural ODE on the Lie group SE(3), and the controller is iteratively optimized.
On the use of energy tanks for robotic systems
In this document we describe and discuss energy tanks, a control algorithm which has gained popularity inside the robotics and control community over the last years.
Optimal Energy Shaping via Neural Approximators
We introduce optimal energy shaping as an enhancement of classical passivity-based control methods.
Port-Hamiltonian Modeling of Ideal Fluid Flow: Part II. Compressible and Incompressible Flow
Starting from the group of diffeomorphisms as a configuration space for the fluid, the Stokes Dirac structure is derived by Poisson reduction and then augmented by boundary ports and distributed ports.
Fluid Dynamics Mathematical Physics Differential Geometry Mathematical Physics
Port-Hamiltonian Modeling of Ideal Fluid Flow: Part I. Foundations and Kinetic Energy
In this two-parts paper, we present a systematic procedure to extend the known Hamiltonian model of ideal inviscid fluid flow on Riemannian manifolds in terms of Lie-Poisson structures to a port-Hamiltonian model in terms of Stokes-Dirac structures.
Differential Geometry Mathematical Physics Mathematical Physics Fluid Dynamics
Port-Hamiltonian Approach to Neural Network Training
Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations.
