It’s more advanced and powerful than typical artificial neural networks because it’s actually two neural networks in one, processing data in two parallel networks. DeepOnet is known as a deep neural operator. Called DeepOnet, the model is a type of artificial neural network, which uses interconnected nodes in successive layers that roughly mimic the connections made by neurons in the human brain. That’s critical to the machine learning model the researchers used in the study.
At the most basic level, they allow more to be done with less. These types of statistical algorithms are not only able to analyze data input into them, but more importantly, they can learn from the information to label new relevant data points that are equally or even more important to the outcome that’s being calculated. The researchers found the answer in a sequential sampling technique called active learning. The question that we tackle in the paper is: What is the best possible data that we can use to minimize the number of data points we need?” We don’t have enough samples from the past to predict them further into the future. “An outburst of a pandemic like COVID-19, environmental disaster in the Gulf of Mexico, an earthquake, huge wildfires in California, a 30-meter wave that capsizes a ship - these are rare events and because they are rare, we don’t have a lot of historical data. “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.
This combination allowed them to predict scenarios, probabilities, and even timelines of rare events despite a lack of historical data.ĭoing 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. 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.
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