The researchers utilize an advanced machine learning method known as next generation reservoir computing. While the past may be a fixed and unchangeable point, machine learning can sometimes make predicting the future easier. Researchers at The Ohio State University have recently discovered a new way to predict the behavior of spatiotemporal chaotic systems, such as changes in Earth's weather, that are particularly difficult for scientists to forecast using a new type of machine learning technique called next generation reservoir computing. The research, which was recently published in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, makes use of a brand-new, highly efficient algorithm that, when combined with next-generation reservoir computing, can learn spatiotemporal chaotic systems in a fraction of the time required by traditional machine learning algorithms. Researchers put their method to the test by predicting the behavior of an atmospheric weather model, a challenging problem that has been researched extensively in the past.

Provided by ScienceAlert Storm clouds over a country road The vast number-crunching capabilities of artificial intelligence systems mean we can better predict the future of chaotic systems based on fewer and fewer patterns of the past – and a new algorithm is adding even more accuracy to the process. Developed through next-gen reservoir computing techniques, which take a more dynamic, speedier approach to machine learning, the new algorithm improves predictions of complex physical processes such as the global weather forecast. Calculations of these processes – known as spatiotemporal chaotic systems – can now be done in a fraction of the time, with greater accuracy, using fewer computational resources, and based on less training data. "This is very exciting, as we believe it's a substantial advance in terms of data processing efficiency and prediction accuracy in the field of machine learning," says physicist Wendson de sa Barbosa, from Ohio State University. Machine learning is exactly that: computer algorithms using a discovery process to make predictions (such as future weather patterns) based on large data archives (such as past weather patterns).

The vast number-crunching capabilities of artificial intelligence systems mean we can better predict the future of chaotic systems based on fewer and fewer patterns of the past – and a new algorithm is adding even more accuracy to the process. Developed through next-gen reservoir computing techniques, which take a more dynamic, speedier approach to machine learning, the new algorithm improves predictions of complex physical processes such as the global weather forecast. Calculations of these processes – known as spatiotemporal chaotic systems – can now be done in a fraction of the time, with greater accuracy, using fewer computational resources, and based on less training data. "This is very exciting, as we believe it's a substantial advance in terms of data processing efficiency and prediction accuracy in the field of machine learning," says physicist Wendson de sa Barbosa, from Ohio State University. Machine learning is exactly that: computer algorithms using a discovery process to make predictions (such as future weather patterns) based on large data archives (such as past weather patterns).

The past may be a fixed and immutable point, but with the help of machine learning, the future can at times be more easily divined. Using a new type of machine learning method called next generation reservoir computing, researchers at The Ohio State University have recently found a new way to predict the behavior of spatiotemporal chaotic systems – such as changes in Earth's weather – that are particularly complex for scientists to forecast. The study, published today in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, utilizes a new and highly efficient algorithm that, when combined with next generation reservoir computing, can learn spatiotemporal chaotic systems in a fraction of the time of other machine learning algorithms. Researchers tested their algorithm on a complex problem that has been studied many times in the past – forecasting the behavior of an atmospheric weather model. In comparison to traditional machine learning algorithms that can solve the same tasks, the Ohio State team's algorithm is more accurate, and uses 400 to 1,250 times less training data to make better predictions than its counterpart. Their method is also less computationally expensive; while solving complex computing problems previously required a supercomputer, they used a laptop running Windows 10 to make predictions in about a fraction of a second – about 240,000 times faster than traditional machine learning algorithms.