Machine Learning and Chaos

Quite remarkable, if the results can be applied to real world engineering problems.

Machine Learning’s ‘Amazing’ Ability to Predict Chaos by By Natalie Wolchover in Quanta Mag

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.
alf a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.

But now the robots are here to help.

In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.

“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany. ... " 

No indication in the paper abstract of how far such predictions can extend.   Or how their prediction value has been tested.     Technical:    https://arxiv.org/abs/1710.07313    A Challenge still it appears.

See also previous links to signal processing.