1 code implementation • 19 Jan 2023 • Eva Dierkes, Christian Offen, Sina Ober-Blöbaum, Kathrin Flaßkamp
Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems.
1 code implementation • 20 Nov 2022 • Yana Lishkova, Paul Scherer, Steffen Ridderbusch, Mateja Jamnik, Pietro Liò, Sina Ober-Blöbaum, Christian Offen
By one of the most fundamental principles in physics, a dynamical system will exhibit those motions which extremise an action functional.
1 code implementation • 8 Apr 2021 • Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, Karlson Pfannschmidt
While the classical schemes apply very generally and are highly efficient on regular systems, they can behave sub-optimal when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems.
1 code implementation • 10 Nov 2020 • Steffen Ridderbusch, Christian Offen, Sina Ober-Blöbaum, Paul Goulart
Recent advances in learning techniques have enabled the modelling of dynamical systems for scientific and engineering applications directly from data.
1 code implementation • 25 Jun 2020 • Robert I McLachlan, Christian Offen
Therefore, we study symmetric solutions of discretized partial differential equations that arise from a discrete variational principle.
Numerical Analysis Numerical Analysis 65D30, 70H25, 70H50, 35A15, 35B06, 35C07, 37K58