From Disorganized Data to Emergent Dynamic Models: Questionnaires to Partial Differential Equations

Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate the data-driven derivation of parameter dependent, evolutionary partial differential equation (PDE) models capable of generating the data. This type of data is reminiscent of shuffled (multidimensional) puzzle tiles. The for the evolution equations (their "space" and "time") as well as their effective parameters are all , i.e. determined in a data-driven way from our disorganized observations of behavior in them. We use a diffusion map based approach to build a smooth parametrization of our emergent space/time/parameter space for the data. This approach iteratively processes the data by successively observing them on the "space," the "time" and the "parameter" axes of a tensor. Once the data become organized, we use machine learning (here, neural networks) to approximate the operators governing the evolution equations in this emergent space. Our illustrative examples are based (i) on a simple advection-diffusion model; (ii) on a previously developed vertex-plus-signaling model of embryonic development; and (iii) on two complex dynamic network models (one neuronal and one coupled oscillator model) for which no obvious smooth embedding geometry is known a priori. This allows us to discuss features of the process like symmetry breaking, translational invariance, and autonomousness of the emergent PDE model, as well as its interpretability.