Data-driven discovery and parameter estimation of mathematical models in biological pattern formation
Fig 6
Parameter estimation of the Turing model.
(A) The process of Simulation-Decoupled Neural Posterior Estimation(SD-NPE). For prediction, each data sample is input into Natural Gradient Boosting (NGBoost) individually, which outputs a posterior distribution of parameters for that sample. By integrating these posterior distributions with precomputed approximate prior distributions, we can approximate the posterior distribution of parameters across all samples. (B) An example of the estimation of parameters fv and gv of the Turing model. Left: the approximated posterior distribution and the plots of points of target parameters and parameters with high and low probability. Right: Each line shows three examples of the Turing model images corresponding to the target, high, and low probability parameters, respectively.