A team of researchers has developed a new framework which utilizes advanced machine learning and statistical algorithms to predict rare events without the need for large data sets.
Scientists can use a combination of advanced Brown University and Massachusetts Institute of Technology suggests that it doesn’t have to be that way.
In a study published in Nature Computational Sciencethe researchers explain how they utilized statistical algorithms which require less data for accurate predictions, in combination with a powerful machine learning technique developed at Brown University. This combination allowed them to predict scenarios, probabilities, and even timelines of rare events despite a lack of historical data.
Doing so, the research team found that this new framework can provide a way to circumvent the need for massive amounts of data that are traditionally needed for these kinds of computations, instead essentially boiling down the grand challenge of predicting rare events to a matter of quality over quantity.
“You have to realize that these are stochastic events,” said George Karniadakis, a professor of applied mathematics and engineering at Brown and a study author. “An outburst of a pandemic like DOI: 10.1038/s43588-022-00376-0
The study was led by Ethan Pickering and Themistoklis Sapsis from MIT. DeepOnet was introduced in 2019 by Karniadakis and other Brown researchers. They are currently seeking a patent for the technology. The study was supported with funding from the Defense Advanced Research Projects Agency, the Air Force Research Laboratory, and the Office of Naval Research.