Sina Ober-Blöbaum | DeepAI

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Featured Co-authors

Pietro Liò
159 publications
Eyke Hüllermeier
79 publications
Mateja Jamnik
36 publications
Sebastian Peitz
11 publications
Paul Scherer
9 publications
Christian Offen
9 publications
Kostas Margellos
7 publications
Karlson Pfannschmidt
4 publications
Michael Dellnitz
3 publications
Steffen Ridderbusch
3 publications
Mats Vermeeren
3 publications

∙ 08/09/2023

Learning of discrete models of variational PDEs from data

We show how to learn discrete field theories from observational data of ...

0 Christian Offen, et al. ∙

∙ 02/16/2023

Learning discrete Lagrangians for variationalPDEs from data and detection of travelling waves

The article shows how to learn models of dynamical systems from data whi...

0 Christian Offen, et al. ∙

∙ 01/19/2023

Hamiltonian Neural Networks with Automatic Symmetry Detection

Recently, Hamiltonian neural networks (HNN) have been introduced to inco...

0 Eva Dierkes, et al. ∙

∙ 11/20/2022

Approximate Uncertainty Propagation for Continuous Gaussian Process Dynamical Systems

When learning continuous dynamical systems with Gaussian Processes, comp...

0 Steffen Ridderbusch, et al. ∙

∙ 11/20/2022

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery

By one of the most fundamental principles in physics, a dynamical system...

0 Yana Lishkova, et al. ∙

∙ 03/23/2022

Verification of safety critical control policies using kernel methods

Hamilton-Jacobi reachability methods for safety-critical control have be...

0 Nikolaus Vertovec, et al. ∙

∙ 12/23/2021

Variational integration of learned dynamical systems

The principle of least action is one of the most fundamental physical pr...

0 Sina Ober-Blöbaum, et al. ∙

∙ 08/05/2021

Symplectic integration of learned Hamiltonian systems

Hamiltonian systems are differential equations which describe systems in...

0 Christian Offen, et al. ∙

∙ 04/25/2021

Superconvergence of Galerkin variational integrators

We study the order of convergence of Galerkin variational integrators fo...

0 Sina Ober-Blöbaum, et al. ∙

∙ 04/08/2021

Efficient time stepping for numerical integration using reinforcement learning

Many problems in science and engineering require the efficient numerical...

0 Michael Dellnitz, et al. ∙

∙ 11/10/2020

Learning ODE Models with Qualitative Structure Using Gaussian Processes

Recent advances in learning techniques have enabled the modelling of dyn...

0 Steffen Ridderbusch, et al. ∙

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