Fig. 5: Results of inferring the dynamics of chaotic systems.
From: Learning interpretable network dynamics via universal neural symbolic regression
a Comparison of governing equations inferred by our LCC under different initial conditions on the coupled Lorenz system. b Comparison of predictive states of an attractor with the same initial values, produced by equations inferred by our LLC under different initial conditions. c Coefficient errors between the equations inferred by each method on the Rössler system and the true equation. d Comparison of states of the same attractor, generated by the governing equations inferred by the TPSINDy, LCC, and LCC+TPSINDy on the Rössler system. e Bifurcation diagram of the Rössler system via the Poincaré section method, with the horizontal axis representing the parameter c (ranging from 1 to 6) and the vertical axis representing the states on the second dimension (Xi,2) of an attractor. The discovered equation exhibits the same period-doubling and chaotic phenomenon as the true equation. f Comparison of limit cycle at period-1, i.e., c = 2.5. g Comparison of chaos at c = 5.7.
