Intelligent Generation of Evolutionary Series in a Time‐Variant Physical System via Series Pattern Recognition

Abstract

Intelligent generation of time-variant control series remains the critical challenge for acquiring the desired system evolution, due to the difficulties in perceiving temporal correlation and conducting appropriate feedback propagation. A machine learning (ML) algorithm named time-series generative adversarial network (TSGAN) is developed to overcome the difficulties, by incorporating a long short-term memory (LSTM) kernel for recognizing multirange temporal patterns beyond the Markovian approximation and an adversarial training mechanism for efficient optimization. A variety of time series are examined by temperature-control experiments, and the results demonstrate an exceptional accuracy (>95%, 35% higher than prevalent ML methods) as well as strong transferability and stability of the TSGAN algorithm. The dependence of generation performance on underlying statistical mechanisms associated with different ML algorithms, including the deep neural network (DNN), hidden Markov model (HMM), LSTM, and TSGAN, is elucidated by analyzing the generation quality of characteristic temporal patterns. The capability of generating arbitrarily complex response series opens an opportunity for inverse design of time-variant functionals as strenuously pursued in material science and modern technology.