BEGIN:VCALENDAR VERSION:2.0 PRODID:-//EPSS//Seminars//EN BEGIN:VEVENT UID:seminar-15451@epss.ucla.edu DTSTAMP:20260523T211837Z DTSTART:20260508T153000Z DTEND:20260508T163000Z SUMMARY:Space Physics (293): Zackary Pine –\; Reconstructing Plasma Dynamics from Sparse Observations with Physics-Informed Machine Learning: From Laboratory Experiments to Space LOCATION:e.g.\, 3853 Slichter Hall DESCRIPTION:Speaker: Zackary Pine Affiliation: Physics and Astronomy\, UCLA Date: Friday\, May 8\, 2026 Time: 3:30 PM Abstract Accurately diagnosing and characterizing plasma dynamics in laboratory experiments and space plasmas is essential for advancing basic plasma science. Modern high-repetition-rateexperiments and multi-spacecraft missions provide increasingly rich spatiotemporal measurements\, but computational tools that can fully exploit sparse\, noisy\, and incomplete dataremain lacking. Physics-informed neural networks (PINNs) offer a promising route by combining partial measurements with fundamental plasma equations to reconstruct physicallyconsistent quantities that were not directly measured.In this talk\, I will illustrate this approach using shear Alfvén waves\, a fundamental mode of magnetized plasma that transports electromagnetic energy along magnetic fields and is relevantto auroral energy flow [1]. Using synthetic magnetic-field measurements from fully kinetic particle-in-cell simulations\, we show that PINNs can reconstruct the full two-fluid plasmastate—including electric fields\, ion and electron velocities\, and density perturbations—from sparse magnetic-field data alone. The method achieves approximately 10% relative accuracyunder adequate sampling and remains robust to substantial measurement noise.I will then discuss first applications to experimental measurements of shear Alfvén waves on the Large Plasma Device at UCLA [2]\, including challenges posed by real data. Finally\, I will brieflyoutline how this framework could be extended to multi-spacecraft observations\, where detailed measurements of the magnetic-field and the plasma distribution function are available but verysparse. URL:https://epss.ucla.edu/space-physics-293-zackary-pine-reconstructing-plasma-dynamics-from-sparse-observations-with-physics-informed-machine-learning-from-laboratory-experiments-to-space/ END:VEVENT END:VCALENDAR