Contribution to a conference proceedings

PUBDB-2024-00205

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs

Fernandez Corral, A. (Corresponding author)CFEL*DESY* ; Mendoza, S.CFEL*Extern* ; Yachmenev, A.CFEL*DESY* ; Iske, A.CFEL*Extern* ; Küpper, J.CFEL*XFEL.EU*DESY*

2024

Abstract: 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.

Note: https://bib-pubdb1.desy.de/record/603801

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Contributing Institute(s):

1. CFEL-CMI (FS-CFEL-CMI)
2. Universität Hamburg (UHH)
3. beauftragt von UNI (UNI/CUI)
4. Uni Hamburg / Experimentalphysik (UNI/EXP)

Research Program(s):

1. 631 - Matter – Dynamics, Mechanisms and Control (POF4-631) (POF4-631)
2. HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (2019_IVF-HIDSS-0002) (2019_IVF-HIDSS-0002)
3. AIM, DFG project G:(GEPRIS)390715994 - EXC 2056: CUI: Advanced Imaging of Matter (390715994) (390715994)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2024

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