26 citations · 127 across the 12 of their papers we have counts for
12 papers
Numerical approaches to entangling dynamics from variational principles
Christian Offen, Boris Wembe, Laura Ares +2
In this work, we address the numerical identification of entanglement in dynamical scenarios. To this end, we consider different programs based on the restriction of the evolution…
Multiphoton, multimode state classification for nonlinear optical circuits
Denis A. Kopylov, Christian Offen, Laura Ares +5
We introduce a new classification of multimode states with a fixed number of photons. This classification is based on the factorizability of homogeneous multivariate polynomials an…
Learning of discrete models of variational PDEs from data
Christian Offen, Sina Ober-Blöbaum
We show how to learn discrete field theories from observational data of fields on a space-time lattice. For this, we train a neural network model of a discrete Lagrangian density s…
Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves
Christian Offen, Sina Ober-Blöbaum
The article shows how to learn models of dynamical systems from data which are governed by an unknown variational PDE. Rather than employing reduction techniques, we learn a discre…
Hamiltonian Neural Networks with Automatic Symmetry Detection
Eva Dierkes, Christian Offen, Sina Ober-Blöbaum +1
Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems. Hereby, the s…
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
Yana Lishkova, Paul Scherer, Steffen Ridderbusch +4
By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional. This leads to the formation of the Euler-L…
Variational Learning of Euler-Lagrange Dynamics from Data
Sina Ober-Blöbaum, Christian Offen
The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exh…
Symplectic integration of learned Hamiltonian systems
Christian Offen, Sina Ober-Blöbaum
Hamiltonian systems are differential equations which describe systems in classical mechanics, plasma physics, and sampling problems. They exhibit many structural properties, such a…
Bifurcation preserving discretisations of optimal control problems
Christian Offen, Sina Ober-Blöbaum
The first order optimality conditions of optimal control problems (OCPs) can be regarded as boundary value problems for Hamiltonian systems. Variational or symplectic discretisatio…
Efficient time stepping for numerical integration using reinforcement learning
Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke +4
Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamic…
A Multiobjective MPC Approach for Autonomously Driven Electric Vehicles
Sebastian Peitz, Kai Schäfer, Sina Ober-Blöbaum +3
We present a new algorithm for model predictive control of non-linear systems with respect to multiple, conflicting objectives. The idea is to provide a possibility to change the o…
Multiobjective Optimal Control Methods for Fluid Flow Using Reduced Order Modeling
Sebastian Peitz, Sina Ober-Blöbaum, Michael Dellnitz
In a wide range of applications it is desirable to optimally control a dynamical system with respect to concurrent, potentially competing goals. This gives rise to a multiobjective…