%0 Conference Paper
%A Fernandez Corral, Alvaro
%A Mendoza, Sebastian
%A Yachmenev, Andrey
%A Iske, Armin
%A Küpper, Jochen
%T Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs
%M PUBDB-2024-00205
%P 26
%D 2024
%Z https://bib-pubdb1.desy.de/record/603801
%X We present a computational framework for obtaining multidimensional phase-space solutions of systems of non-linear coupled differential equations, using high-order implicit Runge-Kutta Physics-Informed Neural Networks (IRK-PINNs) schemes. Building upon foundational work originally solving differential equations for fields depending on coordinates [J. Comput. Phys. 378, 686 (2019)],we adapt the scheme to a context where the coordinates are treated as functions. This modification enables us to efficiently solve equations of motion for a particle in an external field. Our scheme is particularly useful for explicitly time-independent and periodic fields. We apply this approach to successfully solve the equations of motion for a mass particle placed in a central force field and acharged particle in a periodic electric field.
%B International Conference on Scientific Computing and Machine Learning 2024
%C 19 Mar 2024 - 23 Mar 2024, Kyoto (Japan)
Y2 19 Mar 2024 - 23 Mar 2024
M2 Kyoto, Japan
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%U https://bib-pubdb1.desy.de/record/601475