GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather

Session

Machine Learning

ML

Aug 17, 2026, 9:30 AM

Fernando de Mendonça - LIT (National Institute for Space Research, São José dos Campos, SP, Brazil)

55. From Pixels to Physics: Reconstructing the Solar Atmosphere with Physics-Informed AI

Dr Robert Jarolim (High Altitude Observatory, NSF NCAR, USA.)

HPC, Data Assimilation & Big Data Analytics

Oral

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,...

57. Individual and Collective Dynamics of Vortical Structures in the Solar Atmosphere

Dr Viktor Fedun (School of Electrical and Electronic Engineering, The University of Sheffield, UK.)

Heliophysics & Space Weather

Oral

In this presentation, we discuss recent advances in understanding vortical plasma motions within the solar atmosphere. Using high-resolution magnetoconvection simulations from the Bifrost, MURaM, and R2D2 codes, together with observational data (SST and Sunrise), and state-of-the-art techniques for identifying plasma flows and transport barriers (machine learning, forward and backward...

52. Machine learning techniques for remote sensing of the Sun and inner heliosphere

Dr Francisco Iglesias (Grupo de Estudios en Heliofísica de Mendoza (GEHMe), Universidad de Mendoza, Argentina)

Machine Learning in Space, Earth & Atmospheric Sciences

Oral

Remote sensing of the Sun and the inner heliosphere remains the primary observational approach for investigating the physical mechanisms underlying the Sun’s short-term variability and its associated space weather phenomena. Given the significant societal and economic implications of space weather, a growing number of current and planned space- and ground-based observatories are dedicated to...

30. The Data-Driven Evolution of Heliophysics: Bridging Physical Mechanisms and Machine Learning in the Inner Magnetosphere

Victor A. Pinto

Space Weather Forecasting & Operations

Oral

Heliophysics was born as a data-driven scientific field. From the discovery of the radiation belts and the confirmation of the existence of the solar wind, with the beginning of the space age, and until the latest discoveries in solar, magnetospheric, and ionospheric dynamics and coupling mechanisms, our field has always relied on the use of abundant in-situ and remote measurements to confirm...

18. The Hainan Ionogram Dataset: Supporting AI-Enabled Spread-F Monitoring, Prediction, and Space Weather Operations

Zheng Wang (National Space Science Center, CAS)

Space Weather Forecasting & Operations

Oral

Equatorial and low-latitude Spread-F (SF) can severely affect radio communication and navigation systems, making timely identification and prediction important for space weather monitoring and operations. Although artificial intelligence (AI) has shown strong potential for automating ionogram analysis, broader progress has been limited by the lack of large-scale, well-annotated, and publicly...

51. The impact of machine learning, AI, and novel analysis tools to better understand the plasma dynamics of the Sun

David B. Jess

Heliophysics & Space Weather

Oral

Solar atmospheric plasmas host highly dynamical magnetohydrodynamic (MHD) processes, including reconnection, shocks, turbulence, and waves, that couple the photosphere, chromosphere, and corona and ultimately seed variability throughout the heliosphere. This talk connects these drivers to their signatures in the solar wind and at Earth’s ionosphere, emphasizing the need for consistent...

53. Wave theories and modelling in the lower solar atmosphere

Dr Gary Verth (School Mathematical and Physical Sciences, University of Sheffield, UK.)

Heliophysics & Space Weather

Oral

In this talk I will review recent progress and the remaining challenges in both modelling and analysing observed magnetohydrodynamic (MHD) waves in the lower solar atmosphere. To model waves in sunspot umbrae, we have had to go beyond the standard cylindrical flux tube model due to the irregular shapes of the MHD waveguides. For sunspots the umbra/penumbra boundary shape is reasonably static...