5 citations · 8 across the 12 of their papers we have counts for
13 papers
On Runge-Kutta convolution quadrature based fractional variational integrators
Khaled Hariz Belgacem, Sina Ober-BlÖbaum, Fernando JimÉnez
Lagrangian systems subject to fractional damping can be incorporated into a variational framework by doubling the state variables and introducing fractional derivatives. Fractional…
A new Lagrangian approach to optimal control of second-order systems
Michael Konopik, Sigrid Leyendecker, Sofya Maslovskaya +2
In this work, we propose and study a new approach to formulate the optimal control problem of second-order differential equations, with a particular interest in those derived from…
Variational integrators for a new Lagrangian approach to control affine systems with a quadratic Lagrange term
Michael Konopik, Rodrigo T. Sato Martín de Almagro, Sofya Maslovskaya +2
In this work, we analyse the discretisation of a recently proposed new Lagrangian approach to optimal control problems of affine-controlled second-order differential equations with…
Adaptive higher order reversible integrators for memory efficient deep learning
Sofya Maslovskaya, Sina Ober-Blöbaum, Christian Offen +2
The depth of networks plays a crucial role in the effectiveness of deep learning. However, the memory requirement for backpropagation scales linearly with the number of layers, whi…
Commutator-free Cayley methods
Boris Wembe, Christian Offen, Sofya Maslovskaya +2
Differential equations posed on quadratic matrix Lie groups arise in the context of classical mechanics and quantum dynamical systems. Lie group numerical integrators preserve the…
Trim turnpikes for optimal control problems with symmetries
Kathrin Flaßkamp, Sofya Maslovskaya, Sina Ober-Blöbaum +1
Motivated by mechanical systems with symmetries, we focus on optimal control problems possessing symmetries. Following recent works, which generalized the classical concept of stat…
Variational multirate integrators
Sina Ober-Blöbaum, Theresa Wenger, Tobias Gail +1
The simulation of systems that act on multiple time scales is challenging. A stable integration of the fast dynamics requires a highly accurate approximation whereas for the simula…
Symplectic Methods in Deep Learning
Sofya Maslovskaya, Sina Ober-Blöbaum
Deep learning is widely used in tasks including image recognition and generation, in learning dynamical systems from data and many more. It is important to construct learning archi…
Fractional variational integrators based on convolution quadrature
Khaled Hariz, Fernando Jiménez, Sina Ober-Blöbaum
Fractional dissipation is a powerful tool to study non-local physical phenomena such as damping models. The design of geometric, in particular, variational integrators for the nume…
Superconvergence of Galerkin variational integrators
Sina Ober-Blöbaum, Mats Vermeeren
We study the order of convergence of Galerkin variational integrators for ordinary differential equations. Galerkin variational integrators approximate a variational (Lagrangian) p…
Learning ODE Models with Qualitative Structure Using Gaussian Processes
Steffen Ridderbusch, Christian Offen, Sina Ober-Blöbaum +1
Recent advances in learning techniques have enabled the modelling of dynamical systems for scientific and engineering applications directly from data. However, in many contexts exp…
Fractional damping through restricted calculus of variations
Fernando Jiménez, Sina Ober-Blöbaum
We deliver a novel approach towards the variational description of Lagrangian mechanical systems subject to fractional damping by establishing a restricted Hamilton's principle. Fr…
C1-continuous space-time discretization based on Hamilton's law of varying action
Janine C. Mergel, Roger A. Sauer, Sina Ober-Blöbaum
We develop a class of C1-continuous time integration methods that are applicable to conservative problems in elastodynamics. These methods are based on Hamilton's law of varying ac…