Plasmas — hot, electrically charged gases that make up most of the visible universe — play a central role in many areas of science and technology, from forecasting space weather to developing revolutionary energy systems like fusion reactors. Laboratory experiments are essential for understanding how plasma behaves, but measuring it in full is extremely difficult. Plasma's high temperature and fast-changing nature mean that only a portion of what is happening can usually be captured, leaving many pieces of the picture missing. This project aims to develop new machine learning (ML) tools that are guided by the laws of physics to help complete that picture. By combining limited measurements from experiments with theoretical models, these tools aim to reconstruct hidden or hard-to-measure aspects of plasma behavior. If successful, this approach will lead to better understanding of plasma dynamics, transforming our ability to extract insight from cutting-edge experiments. The ML tools may also be applied to future multi-satellite space missions, where scattered satellite measurements need to be stitched together into a cohesive view. The project will train the next generation of researchers at the intersection of physics, computation, and machine learning. The Large Plasma Device (LAPD) at UCLA is a unique experimental platform for basic plasma science, enabling studies relevant to space physics, astrophysics, and controlled nuclear fusion. Its high reproducibility, 1 Hz re