While a thunderstorm could knock out your neighborhood’s power for a few hours, a solar storm could knock out electricity grids across all of Earth, possibly taking weeks to recover from.
To try to predict solar storms — which are disturbances on the sun — and their potential effects on Earth, NASA’s Frontier Development Lab (FDL) is running what it calls a geoeffectiveness challenge.
It uses datasets of tracked changes in the magnetosphere — where the Earth’s magnetic field interacts with solar wind — to train AI-powered models that can detect patterns of space weather events and predict their Earth-related impacts.
The training of the models is optimized on NVIDIA GPUs available on Google Cloud, and data exploration is done on RAPIDS, NVIDIA’s open-source suite of software libraries built to execute data science and analytics pipelines entirely on GPUs.
Siddha Ganju, a solutions architect at NVIDIA who was named to Forbes’ 30 under 30 list in 2018, is advising NASA on the AI-related aspects of the challenge.
A deep learning expert, Ganju grew up going to hackathons. She says she’s always been fascinated by how an algorithm can read in between the lines of code.
Now, she’s applying her knowledge to NVIDIA’s automotive and healthcare businesses, as well NASA’s AI technical steering committee. She’s also written a book on practical uses of deep learning, published last October.
Modeling Space Weather Impacts with AI
Ganju’s work with the FDL began in 2017, when its founder, James Parr, asked her to start advising the organization. Her current task, advising the geoeffectiveness challenge, seeks to use machine learning to characterize magnetic field perturbations and model the impact of space weather events.
In addition to solar storms, space weather events can include such activities as solar flares, which are sudden flashes of increased brightness on the sun, and solar wind, a stream of charged particles released from it.
Not all space weather events impact the Earth, said Ganju, but in case one does, we need to be prepared. For example, a single powerful solar storm could knock out our planet’s telephone networks.
“Even if we’re able to predict the impact of an event just 15 minutes in advance, that gives us enough time to sound the alarm and prepare for potential connectivity loss,” said Ganju. “This data can also be useful for satellites to communicate in a better way.”
Exploring Spatial and Temporal Patterns
Solar events can impact parts of the Earth differently due to a variety of factors, Ganju said. With the help of machine learning, the FDL is trying to find spatial and temporal patterns of the effects.
“The datasets we’re working with are huge, since magnetometers collect data on the changes of a magnetic field at a particular location every second,” said Ganju. “Parallel processing using RAPIDS really accelerates our exploration.”
In addition to Ganju, researchers Asti Bhatt, Mark Cheung and Ryan McGranaghan, as well as NASA’s Lika Guhathakurta, are advising the geoeffectiveness challenge team. Its members include Téo Bloch, Banafsheh Ferdousi, Panos Tigas and Vishal Upendran.
The researchers use RAPIDS to explore the data quickly. Then, using the PyTorch and TensorFlow software libraries, they train the models for experiments to identify how the latitude of a location, the atmosphere above it, or the way sun rays hit it affect the consequences of a space weather event.
They’re also studying whether an earthly impact happens immediately as the space event occurs, or if it has a delayed effect, as an impact could depend on time-related factors, such as the Earth’s revolutions around the sun or its rotation about its own axis.
To detect such patterns, the team will continue to train the model and analyze data throughout the duration of FDL’s eight-week research sprint, which concludes later this month.
Other FDL projects participating in the sprint, according to Ganju, include the moon for good challenge, which aims to discover the best landing position on the moon. Another is the astronaut health challenge, which is investigating how high-radiation environments can affect an astronaut’s well-being.
Feature image courtesy of NASA.
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