text:   systems",5,"['Happy to briefly discuss the new work led by @valedand about exploiting information theory to compress the phase space of nonlinear dynamical systems and detect state changes. Paper 👉 <LINK>\n\nThread 👇 1 / <LINK>', ""Since the '80s we know how to reconstruct the phase space of a dynamical system from the observation of the series it generates. Methods mostly depend on two unknowns: embedding dimension and time delay. We propose compressibility of the dynamics to solve this issue: it works! 2/ https://t.co/TbGrNT0Fjr"", 'So we started to analyze more complex dynamics, such as coupled chaotic oscillators of different nature and varied their coupling. We observe how the effective embedding dimension change &amp; reveal changes that Lyapunov or Correlation Dim analyses do not reveal separately. 3/ https://t.co/EqneJN1KWo', ""When we do the same analysis for coupled chaotic maps, results are similar: we have to use *both* Lyapunov and CorDim to understand what's going on, whereas our embedding dimension is self-consistent. Why that's relevant?  4/ https://t.co/LMEFs8n7d3"", 'Because calculating Lyapunov exponents and CorDim requires long + possibly non-noisy time series, whereas we demonstrate that our method works faster and with smaller observational size: we exploit that for causal interactions information entropy grows non-extensively. 5/5']",20,06,1286