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Southwest Research Institute News

In a significant advancement for heliophysics, scientists have presented a new AI-powered prediction tool that may revolutionize our capacity to defend Earth against the Sun’s erratic eruptions. Physics-Informed Neural Network-Based Active Regions Distribution Simulator, or PINNBARDS, is the result of a collaborative effort between the Southwest Research Institute (SwRI) and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR). This approach is a major step forward from current capabilities, which frequently only offer hours of warning, in terms of forecasting extreme space weather weeks in advance.

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A Revolution in Lead Time

For many years, contemporary technological society has been constantly threatened by the Sun’s unpredictable nature. The resultant explosions of solar particles from the Sun’s explosive events, such as solar flares and coronal mass ejections (CMEs), can seriously damage Earth’s infrastructure. In addition to posing serious health concerns to astronauts in orbit, these occurrences have a wide-ranging impact and endanger satellite communications, electrical grids, and GPS systems.

Small-scale magnetic signature detection is a major component of current forecasting, which only becomes predictive a few hours prior to an eruption. The possibility of significantly longer lead times is provided by PINNBARDS, nevertheless. The technology would provide government organizations and private sector businesses weeks’ warning to put important mitigation plans into place, protecting vital infrastructure and future human space travel.

Peering into the Solar Interior

Connecting surface measurements of solar active regions (ARs) to the deep magnetic dynamics that are concealed far under the Sun’s visible exterior is the main innovation of PINNBARDS. For a long time, scientists have struggled to comprehend these active regions, which are spots with tangled magnetic fields.

Dr. Subhamoy Chatterjee, a SwRI early-career scientist and co-author of the study, said, “Determining where and when large, flare-producing active regions on the Sun would emerge is a long-standing problem in heliophysics.”

Solar active zones do not appear randomly, the research team showed. Rather, they tend to group together along “toroidal bands,” which are enormous, distorted magnetic bands. To reconstruct the magnetic states deep within the Sun, the researchers demonstrated that surface patterns could be “inverted” using magnetic observations from the Helioseismic and Magnetic Imager of the Solar Dynamics Observatory.

In particular, the tachocline a narrow transition layer that separates the Sun’s turbulent outer convection zone from its uniformly revolving radiative interior is investigated by PINNBARDS. The Sun’s magnetic development depends on this area.

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The Power of Physics-Informed AI

In contrast to conventional AI models that might just use data patterns, PINNBARDS is a neural network that is informed by physics. This implies it incorporates well-established physical models of solar dynamics with global solar data.

“This work allows us to look beneath the Sun’s surface and reconstruct the magnetic conditions that give rise to those regions by combining physics-based modeling with AI,” said Todd Hoeksema, a professor at Stanford University and the head of the COFFIES DRIVE Center.

To provide initial circumstances for forward simulations, the tool creates rebuilt subsurface states. Scientific simulations can predict the advent of massive flare-producing zones with unparalleled foresight. Identifying emerging regions’ latitude and longitude is crucial for predicting if a solar particle burst will reach Earth.

Collaborative Science and Supercomputing

It took a lot of teamwork and enormous processing power to develop PINNBARDS. For code creation, testing, and production runs, the study made use of the NSF NCAR-Wyoming Supercomputer Center’s Derecho supercomputer.

The team, led by Mausumi Dikpati, a senior scientist at NSF NCAR, has published their findings in The Astrophysical Journal. Numerous high-level programs, such as NASA’s Heliophysics Guest Investigator Open (HGIO) program and the Consequences of Fields and Flows in the Interior and Exterior of the Sun (COFFIES) DRIVE Center, a NASA-funded project dedicated to unraveling the secrets of the solar interior, provided support for the work.

Protecting a Technical Society

The demand for sophisticated space weather alerts is growing as human activity spreads farther into space and our dependence on orbital technology increases. According to the research team, PINNBARDS will serve as the basis for a new generation of forecasting instruments that are intended to predict severe space weather.

To predict the Sun’s next major move, scientists are now gaining the upper hand by bridging the gap between what we see on the Sun’s surface and the intricate magnetic engines driving those movements from within. The technology is a first step, but it paves the way for a time when space weather is a controllable environmental condition rather than an unexpected occurrence.

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