text: 439,55,1275773294168473600,572479189,Manlio De Domenico,"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> Thread 👇 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/ <LINK> 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 & reveal changes that Lyapunov or Correlation Dim analyses do not reveal separately. 3/ <LINK> 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/ <LINK> 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",https://arxiv.org/abs/2006.12842,"Equations governing the nonlinear dynamics of complex systems are usually unknown and indirect methods are used to reconstruct their manifolds. In turn, they depend on embedding parameters requiring other methods and long temporal sequences to be accurate. In this paper, we show that an optimal reconstruction can be achieved by lossless compression of system's time course, providing a self-consistent analysis of its dynamics and a measure of its complexity, even for short sequences. Our measure of complexity detects system's state changes such as weak synchronization phenomena, characterizing many systems, in one step, integrating results from Lyapunov and fractal analysis. ","Compressing phase space detects state changes in nonlinear dynamical