Thursday, January 4, 2024

"Machine Learning’s ‘Amazing’ Ability to Predict Chaos"

A repost from 2018:

When you have one complex-chaotic system, say an ag or energy derivatives market overlaid on another complex-chaotic system, say, for example, weather; the ability to foretell the progression from the initial condition of one, or better yet both, systems would have some pecuniary advantage
Researchers have used machine learning to predict the chaotic evolution of a model flame front.

Hey, I've made that bet! It's called "The ol' just light large-denomination banknotes on fire to avoid the hassle of feigning any type of skill or expertise in  weird instruments you don't understand trade."**

From Quanta Magazine:
In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.
Half 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.
The findings come from veteran chaos theorist Edward Ott and four collaborators at the University of Maryland. They employed a machine-learning algorithm called reservoir computing to “learn” the dynamics of an archetypal chaotic system called the Kuramoto-Sivashinsky equation. The evolving solution to this equation behaves like a flame front, flickering as it advances through a combustible medium. The equation also describes drift waves in plasmas and other phenomena, and serves as “a test bed for studying turbulence and spatiotemporal chaos,” said Jaideep Pathak, Ott’s graduate student and the lead author of the new papers....MUCH MORE