{'fraction_non_alphanumeric': 0.017173051519154558, 'fraction_numerical': 0.0, 'mean_word_length': 5.767857142857143, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Identification of coupled map lattice models of stochastic spatio-temporal dynamics using wavelets

This paper introduces a new approach for the local reconstruction of coupled map lattice (CML) models of stochastic spatio-temporal dynamics from measured data. The nonlinear functionals describing the evolution of the spatio-temporal patterns are constructed using B-spline wavelet and scaling functions. This provides a multiresolution approximation for the underlying spatio-temporal dynamics. An orthogonal least squares algorithm is used to determine significant terms from wavelet functions to form an accurate representation of the nonlinear spatio-temporal dynamics. Two examples are used to demonstrate the application of the proposed new approach.