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By NASA
6 min read
NASA, IBM’s ‘Hot’ New AI Model Unlocks Secrets of Sun
This image from June 20, 2013 shows the bright light of a solar flare and an eruption of solar material shooting through the sun’s atmosphere, called a prominence eruption. Shortly thereafter, this same region of the sun sent a coronal mass ejection out into space — a phenomenon which can cause magnetic storms that degrade communication signals and cause unexpected electrical surges in power grids on Earth. NASA’s new heliophysics AI foundation model, Surya, can help predict these storms. NASA/Goddard/SDO NASA is turning up the heat in solar science with the launch of the Surya Heliophysics Foundational Model, an artificial intelligence (AI) model trained on 14 years of observations from NASA’s Solar Dynamics Observatory.
Developed by NASA in partnership with IBM and others, Surya uses advances in AI to analyze vast amounts of solar data, helping scientists better understand solar eruptions and predict space weather that threatens satellites, power grids, and communication systems. The model can be used to provide early warnings to satellite operators and helps scientists predict how the Sun’s ultraviolet output affects Earth’s upper atmosphere.
Preliminary results show Surya is making strides in solar flare forecasting, a long-standing challenge in heliophysics. Surya, with its ability to generate visual predictions of solar flares two hours into the future, marks a major step towards the use of AI for operational space weather prediction. These initial results surpass existing benchmarks by 15%. By providing open access to the model on HuggingFace and the code on GitHub, NASA encourages the science and applications community to test and explore this AI model for innovative solutions that leverage the unique value of continuous, stable, long-duration datasets from the Solar Dynamics Observatory.
Illustrations of Solar Dynamics Observatory solar imagery used for training Surya: Solar coronal ultraviolet and extreme ultraviolet images from the Atmospheric Imaging Assembly (AIA) and solar surface velocity and magnetic field maps from the Helioseismic and Magnetic Imager (HMI). NASA/SDO The model’s success builds directly on the Solar Dynamics Observatory’s long-term database. Launched in 2010, NASA’s Solar Dynamics Observatory has provided an unbroken, high-resolution record of the Sun for nearly 15 years through capturing images every 12 seconds in multiple wavelengths, plus precise magnetic field measurements. This stable, well-calibrated dataset, spanning an entire solar cycle, is uniquely suited for training AI models like Surya, enabling them to detect subtle patterns in solar behavior that shorter datasets would miss.
Surya’s strength lies in its foundation model architecture, which learns directly from raw solar data. Unlike traditional AI systems that require extensive labeling, Surya can adapt quickly to new tasks and applications. Applications include tracking active regions, forecasting flare activity, predicting solar wind speed, and integrating data from other observatories including the joint NASA-ESA Solar and Heliospheric Observatory mission and NASA’s Parker Solar Probe.
“We are advancing data-driven science by embedding NASA’s deep scientific expertise into cutting-edge AI models,” said Kevin Murphy, chief science data officer at NASA Headquarters in Washington. “By developing a foundation model trained on NASA’s heliophysics data, we’re making it easier to analyze the complexities of the Sun’s behavior with unprecedented speed and precision. This model empowers broader understanding of how solar activity impacts critical systems and technologies that we all rely on here on Earth.”
These images compare the ground-truth data (right) with model output (center) for solar flares, which are the events behind most space weather. Surya’s prediction is very close to what happened in reality (right). These preliminary results suggest that Surya has learned enough solar physics to predict the structure and evolution of a solar flare by looking at its beginning phase. NASA/SDO/ODSI IMPACT AI Team Solar storms pose significant risks to our technology-dependent society. Powerful solar events energize Earth’s ionosphere, resulting in substantial GPS errors or complete signal loss to satellite communications. They also pose risks to power grids, as geomagnetically induced currents from coronal mass ejections can overload transformers and trigger widespread outages.
In commercial aviation, solar flares can disrupt radio communications and navigation systems while exposing high-altitude flights to increased radiation. The stakes are even higher for human spaceflight. Astronauts bound for the Moon or Mars may need to depend on precise predictions to shelter from intense radiation during solar particle events.
The Sun’s influence extends to the growing number of low Earth orbit satellites, including those that deliver global high-speed internet. As solar activity intensifies, it heats Earth’s upper atmosphere, increasing drag that slows satellites, pulls them from orbit, and causes premature reentry. Satellite operators often struggle to forecast where and when solar flares might affect these satellites.
The “ground truth” solar activity is shown on the top row. The bottom row shows solar activity predicted by Surya. NASA/SDO/ODSI IMPACT AI Team “Our society is built on technologies that are highly susceptible to space weather,” said Joseph Westlake, Heliophysics Division director at NASA Headquarters. “Just as we use meteorology to forecast Earth’s weather, space weather forecasts predict the conditions and events in the space environment that can affect Earth and our technologies. Applying AI to data from our heliophysics missions is a vital step in increasing our space weather defense to protect astronauts and spacecraft, power grids and GPS, and many other systems that power our modern world.”
While Surya is designed to study the Sun, its architecture and methodology are adaptable across scientific domains. From planetary science to Earth observation, the project lays the foundational infrastructure for similar AI efforts in diverse domains.
Surya is part of a broader NASA push to develop open-access, AI-powered science tools. Both the model and training datasets are freely available online to researchers, educators, and students worldwide, lowering barriers to participation and sparking new discoveries.
The process for creating Surya. Foundation models enhance the utility of NASA’s Solar Dynamics Observatory datasets and create a base for building new applications. NASA/ODSI IMPACT AI Team Surya’s training was supported in part by the National Artificial Intelligence Research Resource (NAIRR) Pilot, a National Science Foundation (NSF)-led initiative that provides researchers with access to advanced computing, datasets, and AI tools. The NAIRR Pilot brings together federal and industry resources, such as computing power from NVIDIA, to expand access to the infrastructure needed for cutting-edge AI research.
“This project shows how the NAIRR Pilot is uniting federal and industry AI resources to accelerate scientific breakthroughs,” said Katie Antypas, director of NSF’s Office of Advanced Cyberinfrastructure. “With support from NVIDIA and NSF, we’re not only enabling today’s research, we’re laying the groundwork for a national AI network to drive tomorrow’s discoveries.”
Surya is part of a larger effort championed and supported by NASA’s Office of the Chief Science Data Officer and Heliophysics Division, the NSF , and partnering universities to advance NASA’s scientific missions through innovative data science and AI models. Surya’s AI architecture was jointly developed by the Interagency Implementation and Advanced Concepts Team (IMPACT) under the Office of Data Science and Informatics at NASA’s Marshall Space Flight Center in Huntsville, Alabama; IBM; and a collaborative science team.
The science team, assembled by NASA Headquarters, consisted of experts from the Southwest Research Institute in San Antonio, Texas; the University of Alabama in Huntsville in Huntsville, Alabama; the University of Colorado Boulder in Boulder, Colorado; Georgia State University in Atlanta, Georgia; Princeton University in Princeton, New Jersey; NASA’s SMD’s Heliophysics Division; NASA’s Goddard Space Flight Center in Greenbelt, Maryland; NASA’s Jet Propulsion Laboratory in Pasadena, California; and the SETI Institute in Mountain View, California.
For a behind-the-scenes dive into Surya’s architecture, industry and academic collaborations, challenges behind developing the model, read the blog post on NASA’s Science Data Portal:
https://science.data.nasa.gov/features-events/inside-surya-solar-ai-model
For more information about NASA’s strategy of developing foundation models for science, visit:
https://science.nasa.gov/artificial-intelligence-science
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Last Updated Aug 20, 2025 Related Terms
Science & Research Artificial Intelligence (AI) Heliophysics Solar Dynamics Observatory (SDO) The Sun The Sun & Solar Physics Explore More
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By NASA
2 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
Here you see the X-59 scaled model inside the JAXA supersonic wind tunnel during critical tests related to sound predictions.JAXA Researchers from NASA and the Japanese Aerospace Exploration Agency (JAXA) recently tested a scale model of the X-59 experimental aircraft in a supersonic wind tunnel located in Chofu, Japan, to assess the noise audible underneath the aircraft.
The test was an important milestone for NASA’s one-of-a-kind X-59, which is designed to fly faster than the speed of sound without causing a loud sonic boom.
When the X-59 flies, sound underneath it – a result of its pressure signature – will be a critical factor for what people hear on the ground.
The X-59 is 99.7 feet long, with a wingspan of 29.7 feet. The JAXA wind tunnel, on the other hand, is just over 3 feet long by 3 feet wide.
So, researchers used a model scaled to just 1.62% of the actual aircraft – about 19 inches nose-to-tail. They exposed it to conditions mimicking the X-plane’s planned supersonic cruising speed of Mach 1.4, or approximately 925 miles per hour.
The series of tests performed at JAXA allowed NASA researchers to gather critical experimental data to compare to their predictions derived through Computational Fluid Dynamics modeling, which include how air will flow around the aircraft.
This marked the third round of wind tunnel tests for the X-59 model, following a previous test at JAXA and at NASA’s Glenn Research Center in Ohio.
The data will help researchers understand the noise level that will be created by the shock waves the X-59 produces at supersonic speeds.
The shock waves from traditional supersonic aircraft typically merge together, producing a loud sonic boom. The X-59’s unique design works to keep shock waves from merging, will result in a quieter sonic thump.
The X-59 was built in Palmdale, California at contractor Lockheed Martin Skunk Works and is undergoing final ground tests en route to its historic first flight this year.
NASA’s Quesst mission aims to help change the future of quiet supersonic travel using the X-59. The experimental aircraft allow the Quesst team to gather public feedback on acceptable sound levels for quiet supersonic flight.
Through Quesst’s development of the X-59, NASA will deliver design tools and technology for quiet supersonic airliners that will achieve the high speeds desired by commercial operators without creating disturbance to people on the ground.
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Last Updated Jul 11, 2025 EditorLillian GipsonContactJim Bankejim.banke@nasa.gov Related Terms
Aeronautics Aeronautics Research Mission Directorate Low Boom Flight Demonstrator Quesst (X-59) Quesst: The Vehicle Supersonic Flight View the full article
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By NASA
2 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA’s Sustainable Flight Demonstrator project concluded wind tunnel testing in the fall of 2024. Tests on a Boeing-built X-66 model were completed at NASA’s Ames Research Center in California’s Silicon Valley in its 11-Foot Transonic Unitary Plan Facility. The model underwent tests representing expected flight conditions to obtain engineering information to influence design of the wing and provide data for flight simulators.NASA/Brandon Torres Navarrete NASA’s Sustainable Flight Demonstrator (SFD) project recently concluded wind tunnel tests of its X-66 semi-span model in partnership with Boeing. The model, designed to represent half the aircraft, allows the research team to generate high-quality data about the aerodynamic forces that would affect the actual X-66.
Test results will help researchers identify areas where they can refine the X-66 design – potentially reducing drag, enhancing fuel efficiency, or adjusting the vehicle shape for better flying qualities.
Tests on the Boeing-built X-66 semi-span model were completed at NASA’s Ames Research Center in California’s Silicon Valley in its 11-Foot Transonic Unitary Plan Facility. The model underwent tests representing expected flight conditions so the team could obtain engineering information to influence the design of the aircraft’s wing and provide data for flight simulators.
NASA’s Sustainable Flight Demonstrator project concluded wind tunnel testing in the fall of 2024. Tests on a Boeing-built X-66 model were completed at NASA’s Ames Research Center in California’s Silicon Valley in its 11-Foot Transonic Unitary Plan Facility. Pressure points, which are drilled holes with data sensors attached, are installed along the edge of the wing and allow engineers to understand the characteristics of airflow and will influence the final design of the wing.NASA/Brandon Torres Navarrete Semi-span tests take advantage of symmetry. The forces and behaviors on a model of half an aircraft mirror those on the other half. By using a larger half of the model, engineers increase the number of surface pressure measurements. Various sensors were placed on the wing to measure forces and movements to calculate lift, drag, stability, and other important characteristics.
The semi-span tests follow earlier wind tunnel work at NASA’s Langley Research Center in Hampton, Virginia, using a smaller model of the entire aircraft. Engineers will study the data from all of the X-66 wind tunnel tests to determine any design changes that should be made before fabrication begins on the wing that will be used on the X-66 itself.
The SFD project is NASA’s effort to develop more efficient aircraft configurations as the nation moves toward aviation that’s more economically, societally, and environmentally sustainable. The project seeks to provide information to inform the next generation of single-aisle airliners, the most common aircraft in commercial aviation fleets around the world. Boeing and NASA are partnering to develop the X-66 experimental demonstrator aircraft.
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Last Updated Feb 05, 2025 EditorDede DiniusContactSarah Mannsarah.mann@nasa.govLocationArmstrong Flight Research Center Related Terms
Armstrong Flight Research Center Aeronautics Ames Research Center Green Aviation Tech Langley Research Center Sustainable Aviation Explore More
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By NASA
Climate change presents one of the most urgent crises of our time, with increasing threats to life, infrastructure, economies, and ecosystems worldwide. Climate change is no longer a distant concern; its effects are being felt now and are projected to intensify if emissions continue unabated. The consequences are severe and irreversible for people today, with rapidly shrinking glaciers and ice sheets, rising sea levels, and more intense heat waves already occurring. Scientists predict even more profound impacts, such as an increase in the frequency and intensity of wildfires, extended drought periods, and stronger tropical cyclones. By 2100, sea levels could rise by up to 6.5 feet1, displacing coastal communities and disrupting ecosystems. In the U.S., the effects vary by region—wildfires in the West have doubled in area burned, and rising sea levels threaten infrastructure in the Southeast. Innovative, data-driven solutions are essential to mitigate these growing risks. From the unique vantage point in space, NASA collects critical long-term observations of our changing planet. NASA produces vast amounts of Earth system science data from satellites, radars, and ships, as well as model outputs, offering a wealth of opportunities for innovative thinkers to leverage these sources. The Sustainable Business Model Challenge is designed to identify and foster sustainable business models built around NASA’s Earth system science data. This challenge invites entrepreneurs, researchers, startups, and innovators to use NASA’s publicly available climate and Earth system data sources to create sustainable business models to address climate challenges.
Award: $100,000 in total prizes
Open Date: January 16, 2025
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For more information, visit: https://nasabusinesschallenge.org/
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By NASA
Download PDF: Contact Dynamics Predictions Utilizing theNESC Parameterless Contact Model
Modeling the capture of the Mars Sample Return (MSR) Orbiting Sample (OS) involves understanding complex dynamic behavior, which includes the OS making contact against the interior of the capture enclosure. The MSR Program required numerical verification of the contact dynamics’ predictions produced using their commercial software tools. This commercial software used “free” parameters to set up the contact modeling. Free parameters (also known as free variables) are not based on contact physics. The commercial contact model used by MSR
required seven free parameters including a Hertzian contact stiffness, surface penetration, stiffening exponent, penetration velocity, contact damping, maximum penetration depth for the contact damping value, and a smoothing function. An example of a parameter that is not free is coefficient of friction, which is a physics-based parameter. Consider the free parameter, contact stiffness. Contact stiffness is already present in the finite element model’s (FEM) stiffness matrix where the bodies come into contact, and surface penetration is disallowed in a physically realizable contact model, as FEM meshes should not penetrate one another during contact (i.e., the zero-contact limit penetration constraint condition).
As such, with each set of selected free parameters generating a different contact force signature, additional numerical verification is required to guide setting these parameters. Contact modeling is nonlinear. This means that the stiffness matrices of contacting bodies are continuously updated as the bodies come into contact, potentially recontact (due to vibrations), and disengage. The modal properties of contacting bodies continuously change with state transitions (e.g., stick-to-slip). Some contact models have been proposed and incorporated in commercial finite element analysis solvers, and most involve static loading. A relatively smaller number involve dynamics, which has historically proven challenging.
In 2005, NASA conducted a study testing several commercial contact solvers in predicting contact forces in transient dynamic environments. This was necessitated by the Space Shuttle Program (SSP)—after the February 2003 Columbia accident— deciding to include contact dynamics in the Space Shuttle transient coupled loads analysis (CLA) to capture the impact of contact nonlinearities. This rendered the entire CLA nonlinear. The study found major difficulties executing nonlinear CLAs in commercial software. A nonlinear solver developed by the NESC and Applied Structural Dynamics (ASD) that was able to produce physically realizable results was numerically verified by NASA and later experimentally validated as well. This nonlinear solver was subsequently utilized to execute all NASA SSP CLAs (i.e., crewed space flights) from 2005 to the final flight in 2011, as well as currently supporting the SLS Program.
The objective of the MSR contact verification work was to provide data that could be used by the MSR team to help define the free parameters listed above for the commercial tool contact model. The NESC/ASD solver was used to model contact between simple cantilever and free beams, deriving contact forces and relative displacements. These resulting data can be used to determine parameter values for more complex structures. Two of the modeled configurations, one for axial contact (Figure 1) and the other for stick/friction (Figure 2), and sample results from the NESC nonlinear dynamic analyses are presented in Figures 1 and 2.
For information, contact:
Dr. Dexter Johnson dexter.johnson@nasa.gov
Dr. Arya Majed arya.majed@nasa.gov
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