How machine learning can predict rare disasters – such as earthquakes or pandemics

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A team of researchers has developed a new framework that uses advanced machine learning and statistical algorithms to predict rare events without the need for large data sets.

Scholars can use a combination of advanced[{” attribute=””>machine learning and sequential sampling techniques to predict extreme events without the need for large data sets, according to researchers from Brown and

However, a group of scientists from Brown University and Massachusetts Institute of Technology suggests that it doesn’t have to be that way.

In a study published in Nature Computational Science, the 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

In the paper, the research team shows that combined with active learning techniques, the DeepOnet model can get trained on what parameters or precursors to look for that lead up to the disastrous event someone is analyzing, even when there are not many data points.

“The thrust is not to take every possible data and put it into the system, but to proactively look for events that will signify the rare events,” Karniadakis said. “We may not have many examples of the real event, but we may have those precursors. Through mathematics, we identify them, which together with real events will help us to train this data-hungry operator.”

In the paper, the researchers apply the approach to pinpointing parameters and different ranges of probabilities for dangerous spikes during a pandemic, finding and predicting rogue waves, and estimating when a ship will crack in half due to stress. For example, with rogue waves — ones that are greater than twice the size of surrounding waves — the researchers found they could discover and quantify when rogue waves will form by looking at probable wave conditions that nonlinearly interact over time, leading to waves sometimes three times their original size.

The researchers found their new method outperformed more traditional modeling efforts, and they believe it presents a framework that can efficiently discover and predict all kinds of rare events.

In the paper, the research team outlines how scientists should design future experiments so that they can minimize costs and increase the forecasting 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.

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