Benchlab is aFDL’s Iniciative to maintain SOTA in ML FOR science
Space weather shapes everything from satellites to power grids. Yet predicting the solar wind, three days ahead, reliably - remains one of the biggest open problems in heliophysics.
This tournament invites the world’s data scientists, researchers, and curious minds to take a fresh look at this challenge through the lens of machine learning. It is an opportunity to push the frontier of science together and build models that could one day help protect critical infrastructure and future missions.
This initiative aims to bridge the gap between model development and operational deployment by challenging participants to predict ambient solar wind speed three days in advance.
Solar wind prediction is critical for protecting satellites, astronauts, and infrastructure. While recent advances in AI have enabled new predictive methods, operationalizing these models remains a challenge. This tournament aims to test submitted workflows in increasingly realistic operational scenarios.
The Frontier Development Lab (FDL.ai) and the University of Colorado Boulder (SWx-TREC) invite the global data science and heliophysics communities to participate in a high-impact AI-for-science tournament.
Hosted by University of Colorado Boulder and Trillium Technologies
The primary goal is to improve critical space weather capabilities by developing machine learning models that predict ambient solar wind speed three days ahead. Submissions will be tested against real-world, real-time data to benchmark performance against physics-based baselines.
The competition is structured into three iterative "Challenge Laps," designed to simulate the demands of an operational environment. Each lap becomes progressively more demanding, with code updates permitted between phases.