Förger, FynnFynnFörgerJürß, PaulPaulJürßBoberg, MarijaMarijaBobergHau, TimTimHauKnopp, TobiasTobiasKnoppMöddel, MartinMartinMöddel2025-04-112025-04-112025-03-14International Journal on Magnetic Particle Imaging 11 (1, Suppl 1): 2503009 (2005)https://hdl.handle.net/11420/55328Accurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction.en2365-9033International journal on magnetic particle imaging20251, Suppl 1Infinite Science Publishinghttps://creativecommons.org/licenses/by/4.0/Technology::621: Applied PhysicsComputer Science, Information and General Works::004: Computer SciencesNatural Sciences and Mathematics::519: Applied Mathematics, ProbabilitiesCurrent-to-field prediction for non-linear magnetic systems via neural networksJournal Articlehttps://doi.org/10.15480/882.1507210.18416/IJMPI.2025.250300910.15480/882.15072Journal Article