IBM and NASA Develop a Digital Twin of the Sun to Predict Future Solar Storms

"The Sun's most complex mysteries could soon be solved thanks to artificial intelligence. On August 20, IBM and NASA announced the launch of Surya, a foundation model for the sun. Having been trained on large datasets of solar activity, this AI tool aims to deepen humanity's understanding of solar weather and accurately predict solar flares-bursts of electromagnetic radiation emitted by our star that threaten both astronauts in orbit and communications infrastructure on Earth."

"Surya was trained with nine years of data collected by NASA's Solar Dynamics Observatory (SDO), an instrument that has orbited the sun since 2010, taking high-resolution images every 12 seconds. The SDO captures observations of the sun at various different electromagnetic wavelengths to estimate the temperature of the star's layers. It also takes precise measurements of the sun's magnetic field-essential data for understanding how energy moves through the star, and for predicting solar storms."

"Historically, interpreting this vast amount of diverse and complex data has been a challenge for heliophysicists. To address this challenge, IBM says that Surya's developers used the SDO data to create a digital twin of the sun-a dynamic virtual replica of the star that is updated when new data is captured, and which can be manipulated and more easily studied. The process began with unifying the various data formats fed into the model, allowing it to process them consistently. Next, a long-range vision transformer was employed-AI architecture that enables detailed analysis of very high-resolution images and the identification of relationships between their components, regardless of their distance. The model's performance was optimized using a mechanism called spectral gating, which reduces memory usage by up to 5 percent by filtering out noise in the data, thereby increasing the quality of the processed information."

Surya is a foundation AI model trained on nine years of Solar Dynamics Observatory data to improve understanding of solar weather and predict solar flares. The SDO provides high-resolution images every 12 seconds across multiple electromagnetic wavelengths and precise magnetic field measurements to estimate layer temperatures and track energy movement. A dynamic digital twin of the sun is created and updated with new observations for easier manipulation and study. Data formats were unified for consistent processing. A long-range vision transformer enables detailed analysis of very high-resolution images. Spectral gating reduces memory use by filtering noise and improving data quality.

Read at WIRED