15 citations · 42 across the 7 of their papers we have counts for
11 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…