[HN Gopher] Chaos researchers can now predict perilous points of...
       ___________________________________________________________________
        
       Chaos researchers can now predict perilous points of no return
        
       Author : theafh
       Score  : 103 points
       Date   : 2022-09-15 14:19 UTC (8 hours ago)
        
 (HTM) web link (www.quantamagazine.org)
 (TXT) w3m dump (www.quantamagazine.org)
        
       | fedeb95 wrote:
       | Misleading title, second time this week. "Can" becomes a "could
       | in the future" in the article.
        
       | thenanante wrote:
       | Using a chaotic system to predict a chaotic system.
        
       | makach wrote:
       | does that mean we can keep entropy in check?
        
         | tbalsam wrote:
         | Entropy is like Mundo -- it goes where it pleases.
        
           | airstrike wrote:
           | Reading this as I download patch 3.4 for WR, I believe in the
           | simulation just a bit more
        
       | golemotron wrote:
       | I wonder whether this approach can be used to predict the
       | behavior of social systems, given enough historical data.
        
         | sroussey wrote:
         | Phycohistory.
         | 
         | ;)
        
           | golemotron wrote:
           | ..on ML
        
       | c-linkage wrote:
       | This sounds similar to work I did years ago to combine phase-
       | space manifolds with a rule-based expert system to address
       | problems diagnosing failures in mechanical systems exhibiting
       | multi-modal operating regimes.
       | 
       | Hopefully the researchers found a simpler computational method
       | than I did in trying to mate those two systems together. :)
       | 
       | What really caught my attention was the output of a probability
       | curve showing how the system might operate in the never-before-
       | seen regimes once the tipping point was reached. The ability to
       | predict behavior outside the training set is a huge win. My
       | method was only predictive while the the system operated in the
       | training regime; outside that regime it was useless.
        
         | isoprophlex wrote:
         | The researchers appear to use reservoir computing approaches,
         | which usually aren't terribly costly in terms of cpu cycles.
         | 
         | I'm unsure about real life applications though because one of
         | the quoted papers [0] only uses idealized strange attractors or
         | whatever they're called -- only systems described by math.
         | 
         | I'd be very interested to learn how the methods apply to real-
         | world mechanical chaotic systems.
         | 
         | This isn't my field of expertise at all, maybe someone has some
         | experience with this?
         | 
         | [0] https://arxiv.org/pdf/2207.00521.pdf
        
           | rch wrote:
           | Abstract link for convenience:
           | 
           | https://arxiv.org/abs/2207.00521
        
         | uoaei wrote:
         | Did your manifolds incorporate any notion of system dynamics,
         | or was it a simpler curve-fitting procedure?
        
           | c-linkage wrote:
           | I computed a bounding volume in a hyper-dimensional space
           | containing all sensor instruments on the system. The volume
           | was constructed to encompass the entire sensor state space of
           | many previously recorded "normal" operating periods (from
           | startup through steady-state and shutdown).
           | 
           | New operating regimes where then compared to the volume, and
           | any excursions were considered diagnostically relevant
           | conditions.
           | 
           | The cool part (to me, at least) was that the direction of the
           | vector as system state trajectory exited the volume could be
           | put through a classifier that would effectively tell you what
           | went wrong.
        
             | isoprophlex wrote:
             | TBH that doesn't sound too complicated or overengineered,
             | and pretty performant too. Nice solution!
        
       | Animats wrote:
       | Actual paper:
       | https://journals.aps.org/prresearch/abstract/10.1103/PhysRev...
       | 
       | More useful than Quanta Magazine hype.
       | 
       | The basic idea is that you've got a process with feedback that
       | behaves like a chaotic attractor, moving around a lot but staying
       | in a stable regime. Where's the edge of that regime?
       | 
       | Here's a video of a leaky bucket waterwheel that exhibits chaotic
       | behavior.[1] If all you had was a graph of rotational velocity,
       | could you tell when it was about to reverse? Probably. Could you
       | train a machine learning system to do that? Yes.
       | 
       | It's not clear how general a result this is, but undoubtedly
       | someone is already trying it on financial data.
       | 
       | [1] https://www.youtube.com/watch?v=7A_rl-DAmUE
        
         | mellavora wrote:
         | yes, back in 1990-2000. See
         | https://www.econstor.eu/bitstream/10419/40278/1/338823255.pd...
         | though I was thinking of another paper which I couldn't find
         | with a quick google.
         | 
         | See also Mandelbrot, fractals and scaling in finance, 1997
         | https://link.springer.com/book/10.1007/978-1-4757-2763-0
        
       | pishpash wrote:
       | Even looking at the title only you can tell this is from Quantum
       | Mag. What is it about Quantum Mag that produces these
       | pseudoscientific-sounding titles (regardless of content)?
        
       | FunnyBadger wrote:
       | I call BS on this. It would require negating entropy.
        
         | suoduandao2 wrote:
         | Our current understanding of entropy is probably about as
         | accurate as Newton's understanding of gravity, not something we
         | want to get dogmatic about.
        
       ___________________________________________________________________
       (page generated 2022-09-15 23:01 UTC)