Speaker
Description
Artificial Intelligence (AI) is rapidly transforming how we analyze and interpret solar observations, enabling new approaches to long-standing challenges in heliophysics. In particular, recent advances in machine learning provide a pathway from data-driven image analysis toward physically consistent models of the solar atmosphere. Rather than treating observations as isolated measurements, these methods enable the reconstruction of continuous, multi-dimensional representations constrained by both observations and underlying physical principles.
In this presentation, I will discuss how Physics-Informed AI can bridge the gap between observations and models of the solar atmosphere. I will highlight recent developments emerging from ongoing, collective efforts across the heliophysics and space weather communities to leverage machine learning for 3D reconstruction, inversion, magnetic field extrapolation, and multi-instrument data integration. Together, these approaches demonstrate how sparse and heterogeneous observations can be transformed into physically meaningful representations of the solar atmosphere. These methods illustrate a broader paradigm shift—from pixel-level analysis to learning physically meaningful representations of the Sun. Finally, I will outline key open challenges and opportunities for physics-informed AI to enable the next generation of data-driven, physically consistent models of the solar atmosphere.