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*Initial conditions - very important. Just ask grandmother.
A couple variations depending on whether she was feeling chatty or taciturn:
Grandmother would say something like "If the initial condition given is 'The sky is falling', your course of action would be to short sky, try the eggplant"The long version, also re: trajectories, 2013's:
The Future Price Trajectory of Copper and Aluminum and the Implications for Oil
In Which Our Hero Explains the Importance of Recent Events and Their Impact on the Cost of Day-to-Day Living
...For Izabella I believe the story was an intellectual challenge to piece together disparate bits of information into a coherent matrix while battling the forces of darkness (seriously, the word murky doesn't do justice to these markets) and disinformation.
For me it was how to approach the profit possibilities; much in the same way Grandmother would quiz: "Your initial conditions are 'The sky is falling, the sky is falling', what is your course of action?" to which I'd reply "I'm this many: ||||, four" and she'd say, "No silly, your course of action is 'Short sky'"**Tama Churchouse describes his introduction to derivatives sales and the first product he put together and marketed:
"...No matter. I persevered, and three months later, in November I structured and sold my first structured note.
I remember drafting the product term sheet.
I christened it a “Bermudan Callable Three Times Leveraged Inverse HIBOR in-arrears Resettable Step-up Snowball Note.”
No, I’m not kidding…The notional value of the note was HKD100mn (around US$13mn), and we booked around EUR100k of profit...."
